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@ARTICLE{Angelova2008,
  author = {Angelova, Donka and Mihaylova, Lyudmila},
  title = {{Extended object tracking using Monte Carlo methods}},
  journal = {{IEEE TRANSACTIONS ON SIGNAL PROCESSING}},
  year = {{2008}},
  volume = {{56}},
  pages = {{825-832}},
  number = {{2}},
  month = {{FEB}},
  abstract = {{This correspondence addresses the problem of tracking extended objects,
	such as ships or a convoy of vehicles moving in urban environment.
	Two Monte Carlo techniques for extended object tracking are proposed:
	an interacting multiple model data augmentation (IMM-DA) algorithm
	and a modified version of the mixture Kalman filter (MKF) of Chen
	and Liu {[}1], called the mixture Kalman filter modified (MKFm).
	The data augmentation (DA) technique with finite mixtures estimates
	the object extent parameters, whereas an interacting multiple model
	(IMM) filter estimates the kinematic states (position and speed)
	of the manoeuvring object. Next, the system model is formulated in
	a partially conditional dynamic linear (PCDL) form. This affords
	us to propose two latent indicator variables characterizing, respectively,
	the motion mode and object size. Then, an MKFm is developed with
	the PCDL model. The IMM-DA and the MKFm performance is compared with
	a combined IMM-particle filter (IMM-PF) algorithm with respect to
	accuracy and computational complexity. The most accurate parameter
	estimates are obtained by the DA algorithm, followed by the MKFm
	and PF.}},
  doi = {{10.1109/TSP.2007.907851}},
  file = {Angelova2008.pdf:/home/postgres/doctor/lib/Angelova2008.pdf:PDF},
  issn = {{1053-587X}},
  owner = {postgres},
  timestamp = {2011.01.06},
  unique-id = {{ISI:000252575200033}}
}

@ELECTRONIC{aus2008,
  author = {AvNews},
  month = {February--April},
  year = {2008},
  title = {Air Wars Australia -- A View from the Other Side},
  language = {English},
  howpublished = {www.asiapacificbattlespace.com.au},
  organization = {Asia Pacific Battlespace},
  url = {www.ausairpower.net/media.html#APA_NOTAMS},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@ARTICLE{Babacan2010,
  author = {S. Derin Babacan and Rafael Molina and Aggelos K. Katsaggelos},
  title = {Bayesian Compressive Sensing Using Laplace Priors},
  journal = {IEEE Transactions on Signal Processing},
  year = {2010},
  volume = {19},
  pages = {53--63},
  __markedentry = {[postgres:1]},
  file = {Babacan2010.pdf:sbl/Babacan2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.10}
}

@ARTICLE{Babacan2012,
  author = {S. Derin Babacan and Shinichi Nakajima and Minh N. Do},
  title = {Bayesian Group-Sparse Modeling and Variational Inference},
  year = {2012},
  volume = {Sep},
  pages = {1--20},
  __markedentry = {[postgres:1]},
  file = {Babacan2012.pdf:sbl/Babacan2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.13}
}

@MISC{anale55,
  author = {{BAE Systems}},
  title = {{AN/ALE-55 Fiber-Optic Towed Decoy}},
  howpublished = {{BAE Systems}},
  month = {1},
  year = {2005},
  file = {anale55.pdf:anale55.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.19}
}

@ELECTRONIC{bae55,
  author = {{BAE SYSTEMS}},
  year = {1998},
  title = {{IDECM RFCM- AN/ALQ-214 AN/ALE-55}},
  file = {bae55.pdf:trad/bae55.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.25}
}

@ARTICLE{Baraniuk2007,
  author = {Baraniuk, R.G.},
  title = {Compressive Sensing [Lecture Notes]},
  journal = {Signal Processing Magazine, IEEE},
  year = {2007},
  volume = {24},
  pages = {118 -121},
  number = {4},
  month = {july },
  __markedentry = {[postgres:]},
  abstract = {This lecture note presents a new method to capture and represent compressible
	signals at a rate significantly below the Nyquist rate. This method,
	called compressive sensing, employs nonadaptive linear projections
	that preserve the structure of the signal; the signal is then reconstructed
	from these projections using an optimization process.},
  doi = {10.1109/MSP.2007.4286571},
  file = {Baraniuk2007.pdf:sbl/Baraniuk2007.pdf:PDF},
  issn = {1053-5888},
  keywords = {Nyquist rate;compressive sensing;nonadaptive linear projections;optimization
	process;signal capturing;signal reconstruction;signal representation;Nyquist
	criterion;data compression;optimisation;signal reconstruction;signal
	representation;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Baraniuk2010,
  author = {R. G. Baraniuk and V. Cevher and M. F. Duarte and C. Hegde},
  title = {Model-based compressive sensing},
  journal = {IEEE Transactions on Signal Processing},
  year = {2010},
  volume = {56 (4)},
  pages = {1982--2001},
  __markedentry = {[postgres:1]},
  file = {Baraniuk2010.pdf:sbl/Baraniuk2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.10.24}
}

@BOOK{Barshalom2001,
  title = {Estimation with Applications To Tracking and Navigation},
  publisher = {Join Wiley \& Sons, INC.},
  year = {2001},
  author = {Yaakov Bar-Shalom and X.-Rong Li and Thiagalingam Kirubarajan},
  __markedentry = {[postgres:]},
  owner = {postgres},
  review = {P23, The Matrix Inversion Lemma},
  timestamp = {2012.06.06}
}

@INPROCEEDINGS{Baum2009,
  author = {Baum, Marcus and Hanebeck, Uwe D.},
  title = {{Extended Object Tracking based on Combined Set-Theoretic and Stochastic
	Fusion}},
  booktitle = {{FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION,
	VOLS 1-4}},
  year = {{2009}},
  pages = {{1288-1295}},
  note = {{12th International Conference on Information Fusion, Seattle, WA,
	JUL 06-09, 2009}},
  abstract = {{In this paper, a novel approach for tracking extended objects is
	presented. The target object is modeled as a circular disc such that
	the center and extent of the target object can be estimated. At each
	time step, a finite set of position measurements that are corrupted
	with stochastic noise may be available. Each position measurement
	steins from an unknown measurement source on the extended object.
	In contrast to existing approaches, no statistical assumptions about
	the distribution of the measurement sources on the extended object
	are made. As a consequence, it is necessary to deal with stochastic
	and set-valued uncertainties. For this purpose, a novel combined
	stochastic and set-theoretic estimator that employs random hyperboloids
	to express the uncertainties about the true circular disc is derived.}},
  book-group-author = {{IEEE}},
  file = {Baum2009.pdf:/home/postgres/doctor/lib/Baum2009.pdf:PDF},
  isbn = {{978-0-9824-4380-4}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000273560001020}}
}

@INPROCEEDINGS{Baum2009a,
  author = {Baum, Marcus and Hanebeck, Uwe D.},
  title = {{Random Hypersurface Models for Extended Object Tracking}},
  booktitle = {{2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION
	TECHNOLOGY (ISSPIT 2009)}},
  year = {{2009}},
  pages = {{178-183}},
  note = {{9th IEEE International Symposium on Signal Processing and Information
	Technology, Ajman, U ARAB EMIRATES, DEC 14-17, 2009}},
  abstract = {{Target tracking algorithms usually assume that the received measurements
	stem from a point source. However, in many scenarios this assumption
	is not feasible so that measurements may stem from different locations,
	named measurement sources, on the target surface. Then, it is necessary
	to incorporate the target extent into the estimation procedure in
	order to obtain robust and precise estimation results. This paper
	introduces the novel concept of Random Hypersurface Models for extended
	targets. A Random Hypersurface Model assumes that each measurement
	source is an element of a randomly generated hypersurface. The applicability
	of this approach is demonstrated by means of an elliptic target shape.
	In this case, a Random Hypersurface Model specifies the random (relative)
	Mahalanobis distance of a measurement source to the center of the
	target object. As a consequence, good estimation results can be obtained
	even if the true target shape significantly differs from the modeled
	shape. Additionally, Random Hypersurface Models are computationally
	tractable with standard nonlinear stochastic state estimators.}},
  book-group-author = {{IEEE}},
  file = {:/home/postgres/doctor/lib/Baum2009a1.pdf:PDF;Baum2009a.pdf:/home/postgres/doctor/lib/Baum2009a.pdf:PDF},
  isbn = {{978-1-4244-5949-0}},
  owner = {postgres},
  review = {There is two version of this paper : Baum2009a and Baum2009a1.},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000290365400033}}
}

@ARTICLE{Becker2011,
  author = {S. Becker and J. Bobin and E. Candes},
  title = {NESTA: A Fast and Accurate First-Order Method for Sparse Recovery},
  journal = {SIAM Journal on Imaging Sciences},
  year = {2011},
  volume = {4(1)},
  pages = {1--39},
  __markedentry = {[postgres:]},
  file = {Becker2011.pdf:sbl/Becker2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Berg2008,
  author = {Ewout Van Den Berg and Michael P. Friedlander},
  title = {Probing The Pareto Frontier for Basis Pursuit Solutions},
  journal = {SIAM Journal on Scientific Computing},
  year = {2008},
  volume = {31(2)},
  pages = {890--912},
  __markedentry = {[postgres:]},
  file = {Berg2008.pdf:blasso/Berg2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@INPROCEEDINGS{Blom2009,
  author = {Blom, Henk A. P. and Bloem, Edwin A.},
  title = {{Permutation invariance in Bayesian estimation of two targets that
	maneuver in and out formation flight}},
  booktitle = {{FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION,
	VOLS 1-4}},
  year = {{2009}},
  pages = {{1296-1303}},
  note = {{12th International Conference on Information Fusion, Seattle, WA,
	JUL 06-09, 2009}},
  abstract = {{In theory, a good particle filter allows to approximate the exact
	Bayesian filter solution arbitrarily well. This has motivated a strong
	and successful development of particle filtering approaches towards
	target tracking. Literature also shows that successful multiple maneuvering
	target tracking seems to depend on the adoption of some suitable
	heuristics in handling permutation invariance. According to the exact
	Bayesian joint conditional density however, there is no requirement
	to introduce any of these heuristics. In order to improve the insight
	for this kind of problem, this paper studies which role permutation
	invariance plays in the exact joint conditional density of two targets
	that may maneuver in and out formation flight given potentially noisy,
	missing and false measurements.}},
  book-group-author = {{IEEE}},
  file = {Blom2009.pdf:/home/postgres/doctor/lib/Blom2009.pdf:PDF},
  isbn = {{978-0-9824-4380-4}},
  owner = {postgres},
  timestamp = {2011.01.06},
  unique-id = {{ISI:000273560001021}}
}

@TECHREPORT{Bolkcom2005,
  author = {Christopher Bolkcom},
  title = {Tactical Aircraft Modernization: Issues for Congress},
  institution = {Foreign Affaires, Defense, and Trade Division of National Defense},
  year = {2005},
  file = {Bolkcom2005.pdf:Bolkcom2005.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.14}
}

@TECHREPORT{Bolkcom2001,
  author = {Christopher Bolkcom},
  title = {Electronic Warfare: {EA-6B} Aircraft Modernization and Related Issues
	for Congress},
  institution = {Foreign Affaires, Defense, and Trade Division of National Defense},
  year = {2001},
  file = {Bolkcom2001.pdf:Bolkcom2001.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.14}
}

@ARTICLE{Candes2006,
  author = {Candes, E.J. and Romberg, J. and Tao, T.},
  title = {Robust uncertainty principles: exact signal reconstruction from highly
	incomplete frequency information},
  journal = {Information Theory, IEEE Transactions on},
  year = {2006},
  volume = {52},
  pages = { 489 - 509},
  number = {2},
  month = {feb.},
  __markedentry = {[postgres:]},
  abstract = {This paper considers the model problem of reconstructing an object
	from incomplete frequency samples. Consider a discrete-time signal
	f isin;C^N and a randomly chosen set of frequencies Omega;. Is it
	possible to reconstruct f from the partial knowledge of its Fourier
	coefficients on the set Omega;? A typical result of this paper is
	as follows. Suppose that f is a superposition of |T| spikes f(t)=
	sigma;_ tau; isin;T f( tau;) delta;(t- tau;) obeying |T| le;C_M middot;(log
	N)^-1 middot; | Omega;| for some constant C_M >0. We do not know
	the locations of the spikes nor their amplitudes. Then with probability
	at least 1-O(N^-M ), f can be reconstructed exactly as the solution
	to the #8467;_1 minimization problem. In short, exact recovery may
	be obtained by solving a convex optimization problem. We give numerical
	values for C_M which depend on the desired probability of success.
	Our result may be interpreted as a novel kind of nonlinear sampling
	theorem. In effect, it says that any signal made out of |T| spikes
	may be recovered by convex programming from almost every set of frequencies
	of size O(|T| middot;logN). Moreover, this is nearly optimal in the
	sense that any method succeeding with probability 1-O(N^-M ) would
	in general require a number of frequency samples at least proportional
	to |T| middot;logN. The methodology extends to a variety of other
	situations and higher dimensions. For example, we show how one can
	reconstruct a piecewise constant (one- or two-dimensional) object
	from incomplete frequency samples - provided that the number of jumps
	(discontinuities) obeys the condition above - by minimizing other
	convex functionals such as the total variation of f.},
  doi = {10.1109/TIT.2005.862083},
  file = {Candes2006.pdf:sbl/Candes2006.pdf:PDF},
  issn = {0018-9448},
  keywords = { Fourier coefficient; convex optimization; discrete-time signal; image
	reconstruction; incomplete frequency information; linear programming;
	minimization problem; nonlinear sampling theorem; piecewise constant
	object; probability value; robust uncertainty principle; signal reconstruction;
	sparse random matrix; trigonometric expansion; Fourier analysis;
	convex programming; image reconstruction; image sampling; indeterminancy;
	linear programming; minimisation; piecewise constant techniques;
	probability; signal reconstruction; signal sampling; sparse matrices;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Candes2008a,
  author = {Candes, E.J. and Wakin, M.B.},
  title = {An Introduction To Compressive Sampling},
  journal = {Signal Processing Magazine, IEEE},
  year = {2008},
  volume = {25},
  pages = {21 -30},
  number = {2},
  month = {march },
  __markedentry = {[postgres:]},
  abstract = {Conventional approaches to sampling signals or images follow Shannon's
	theorem: the sampling rate must be at least twice the maximum frequency
	present in the signal (Nyquist rate). In the field of data conversion,
	standard analog-to-digital converter (ADC) technology implements
	the usual quantized Shannon representation - the signal is uniformly
	sampled at or above the Nyquist rate. This article surveys the theory
	of compressive sampling, also known as compressed sensing or CS,
	a novel sensing/sampling paradigm that goes against the common wisdom
	in data acquisition. CS theory asserts that one can recover certain
	signals and images from far fewer samples or measurements than traditional
	methods use.},
  doi = {10.1109/MSP.2007.914731},
  file = {Candes2008a.pdf:sbl/Candes2008a.pdf:PDF},
  issn = {1053-5888},
  keywords = {Relatively few wavelet;compressed sensing;compressive sampling;data
	acquisition;image recovery;sampling paradigm;sensing paradigm;signal
	recovery;data acquisition;image processing;signal processing equipment;signal
	sampling;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Candes2008,
  author = {Candes, Emmanuel J. and Wakin, Michael B. and Boyd, Stephen P.},
  title = {{Enhancing Sparsity by Reweighted l(1) Minimization}},
  journal = {{JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS}},
  year = {{2008}},
  volume = {{14}},
  pages = {{877-905}},
  number = {{5-6}},
  month = {{DEC}},
  note = {{4th IEEE International Symposium on Biomedical Imaging, Arlington,
	VA, APR 12-15, 2007}},
  __markedentry = {[postgres:]},
  abstract = {{It is now well understood that (1) it is possible to reconstruct
	sparse signals exactly from what appear to be highly incomplete sets
	of linear measurements and (2) that this can be done by constrained
	l(1) minimization. In this paper, we study a novel method for sparse
	signal recovery that in many situations outperforms l(1) minimization
	in the sense that substantially fewer measurements are needed for
	exact recovery. The algorithm consists of solving a sequence of weighted
	l(1)-minimization problems where the weights used for the next iteration
	are computed from the value of the current solution. We present a
	series of experiments demonstrating the remarkable performance and
	broad applicability of this algorithm in the areas of sparse signal
	recovery, statistical estimation, error correction and image processing.
	Interestingly, superior gains are also achieved when our method is
	applied to recover signals with assumed near-sparsity in overcomplete
	representations-not by reweighting the l(1) norm of the coefficient
	sequence as is common, but by reweighting the l(1) norm of the transformed
	object. An immediate consequence is the possibility of highly efficient
	data acquisition protocols by improving on a technique known as Compressive
	Sensing.}},
  doi = {{10.1007/s00041-008-9045-x}},
  file = {Candes2008.pdf:blasso/Candes2008.pdf:PDF},
  issn = {{1069-5869}},
  organization = {{IEEE}},
  owner = {postgres},
  timestamp = {2012.08.18},
  unique-id = {{ISI:000261411300013}}
}

@ARTICLE{Charles2012,
  author = {Adam S. Charles and Christopher J. Rozell},
  title = {Re-Weighted $\ell_1$ Dynamic Filtering for Time-Varying Sparse Signal
	Estimation},
  journal = {IEEE Transactions on Signal Processing (submitted)},
  year = {2012},
  volume = {1},
  pages = {1--20},
  __markedentry = {[postgres:]},
  file = {Charles2012.pdf:sbl/Charles2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.13}
}

@ARTICLE{Chen1998,
  author = {S. Chen and D. Donoho and M. Saunders},
  title = {Atomic Decomposition by Basis Pursuit},
  journal = {SIAM Journal on Scientific Computing},
  year = {1998},
  volume = {20(1)},
  pages = {33--61},
  __markedentry = {[postgres:1]},
  file = {Chen1998.pdf:sbl/Chen1998.pdf:PDF},
  owner = {postgres},
  review = {UNREAD, REF ONLY},
  timestamp = {2012.08.14}
}

@ARTICLE{Chen2007,
  author = {Zhe Chen and Andrzej Cichocki},
  title = {Nonnegative Matrix Factorization with Temporal Smoothness and/or
	Spatial Decorrelation Constrains},
  journal = {NIPS},
  year = {2007},
  volume = {1},
  pages = {1--10},
  __markedentry = {[postgres:1]},
  file = {Chen2007.pdf:sbl/Chen2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.11.18}
}

@ELECTRONIC{cra2008,
  author = {CRA},
  month = {November},
  year = {2008},
  title = {Top Aerospace \& Defense Programs},
  howpublished = {Aviation Week A\&D Programs Conference},
  organization = {CRA International},
  file = {cra2008.pdf:cra2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.21}
}

@PHDTHESIS{Crouse2011,
  author = {David Frederic Crouse},
  title = {Algorithms for Tracking in Clutter and for sensor Registration},
  school = {University of Connecticut},
  year = {2011},
  file = {Crouse2011.pdf:Crouse2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.11}
}

@ARTICLE{Crouse2012,
  author = {David F. Crouse and Ulrich Nickel and Peter Willett},
  title = {Comments on ``Closed--Form Four--Channel Monopulse Two--Target Resolution''},
  journal = {Aerospace and Electronic Systems, IEEE Transactions on},
  year = {2012},
  volume = {48},
  pages = {912--916},
  file = {Crouse2012.pdf:Crouse2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.07}
}

@ELECTRONIC{typhoon2009,
  author = {DASS},
  month = {May},
  year = {2009},
  title = {Eurofighter technology and performance: defences.},
  url = {http://typhoon.starstreak.net/Eurofighter/defences.html},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@ARTICLE{Davis1990,
  author = {C. W. Davis},
  title = {The Airbone Seeker Test Bed},
  journal = {The Lincoln Laboratory Journal},
  year = {1990},
  volume = {3(2)},
  pages = {203--224},
  file = {Davis1990.pdf:trad/Davis1990.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.25}
}

@ARTICLE{donoho2009observed,
  author = {Donoho, D. and Tanner, J.},
  title = {Observed universality of phase transitions in high-dimensional geometry,
	with implications for modern data analysis and signal processing},
  journal = {Philosophical Transactions of the Royal Society A},
  year = {2009},
  volume = {367},
  pages = {4273--4293},
  number = {1906}
}

@CONFERENCE{Drummond1990,
  author = {Oliver E. Drummond and Samuel S. Blackman and Gregory C. Pretrisor},
  title = {Tracking clusters and extended objects with multiple sensors},
  booktitle = {Signal and Data Processing of Small Targets 1990},
  year = {1990},
  editor = {Oliver E. Drummond},
  volume = {1305},
  number = {1},
  pages = {362-375},
  publisher = {SPIE},
  doi = {10.1117/12.21605},
  file = {:/home/postgres/doctor/lib/drummond1990.pdf:PDF},
  journal = {Signal and Data Processing of Small Targets 1990},
  location = {Orlando, FL, USA},
  owner = {postgres},
  timestamp = {2010.12.30},
  url = {http://link.aip.org/link/?PSI/1305/362/1}
}

@CONFERENCE{Drummond1993,
  author = {Oliver E. Drummond and Gabriel Frenkel},
  title = {Target tracking glossary of the SDI Panels on Tracking},
  booktitle = {Signal and Data Processing of Small Targets 1993},
  year = {1993},
  editor = {Oliver E. Drummond},
  volume = {1954},
  number = {1},
  pages = {606-619},
  publisher = {SPIE},
  doi = {10.1117/12.157811},
  file = {:/home/postgres/doctor/lib/drummond1993.pdf:PDF},
  journal = {Signal and Data Processing of Small Targets 1993},
  location = {Orlando, FL, USA},
  owner = {postgres},
  timestamp = {2010.12.30},
  url = {http://link.aip.org/link/?PSI/1954/606/1}
}

@ARTICLE{Du2008,
  author = {Lin Du and Tarik Yardibi and Jian Li and Petre Stoica},
  title = {Review of User Parameter-Free Robust Adaptive Beamforming Algorithms},
  journal = {Asilomar},
  year = {2008},
  volume = {1},
  pages = {363--367},
  __markedentry = {[postgres:]},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@ARTICLE{Eaglen2010,
  author = {Machenzie Eaglen and Lajos F. Szaszdi},
  title = {What Russia's Stealth Fighter Developments Mean For America},
  journal = {The Heritage Foundation : Backgrounder},
  year = {2010},
  volume = {2494},
  pages = {1--19},
  file = {:eaglen2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.20}
}

@ARTICLE{Eldar2010,
  author = {Y. C. Eldar and P. Kuppinger and H. Bolcskei},
  title = {Block-Sparse signals: uncertainty relations and efficient recovery},
  journal = {IEEE Transaction on Signal Processing},
  year = {2010},
  volume = {58(6)},
  pages = {3042--3054}
}

@ARTICLE{Erdinc2009,
  author = {Erdinc, Ozgur and Willett, Peter and Bar-Shalom, Yaakov},
  title = {{The Bin-Occupancy Filter and Its Connection to the PHD Filters}},
  journal = {{IEEE TRANSACTIONS ON SIGNAL PROCESSING}},
  year = {{2009}},
  volume = {{57}},
  pages = {{4232-4246}},
  number = {{11}},
  month = {{NOV}},
  abstract = {{An algorithm that is capable not only of tracking multiple targets
	but also of ``track management{''}-meaning that it does not need
	to know the number of targets as a user input-is of considerable
	interest. In this paper we devise a recursive track-managed filter
	via a quantized state-space ({''}bin{''}) model. In the limit, as
	the discretization implied by the bins becomes as refined as possible
	(infinitesimal bins) we find that the filter equations are identical
	to Mahler's probability hypothesis density (PHD) filter, a novel
	track-managed filtering scheme that is attracting increasing attention.
	Thus, one contribution of this paper is an interpretation of, if
	not the PHD itself, at least what the PHD is doing. This does offer
	some intuitive appeal, but has some practical use as well: with this
	model it is possible to identify the PHD's ``target-death{''} problem,
	and also the statistical inference structures of the PHD filters.
	To obviate the target death problem, PHD originator Mahler developed
	a new ``cardinalized{''} version of PHD (CPHD). The second contribution
	of this paper is to extend the ``bin-occupancy{''} model such that
	the resulting recursive filter is identical to the cardinalized PHD
	filter.}},
  doi = {{10.1109/TSP.2009.2025816}},
  file = {Erdinc2009.pdf:/home/postgres/doctor/lib/Erdinc2009.pdf:PDF},
  issn = {{1053-587X}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000270747200007}}
}

@ARTICLE{Faul2002,
  author = {Anita C. Faul and Michael E. Tipping},
  title = {Analysis of Sparse Bayesian Learning},
  journal = {Neural Inform. Process. Syst.},
  year = {2002},
  volume = {14},
  pages = {383--389},
  __markedentry = {[postgres:1]},
  file = {Faul2002.pdf:sbl/Faul2002.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@INPROCEEDINGS{Feldmann2009,
  author = {Feldmann, Michael and Fraenken, Dietrich},
  title = {{Advances on Tracking of Extended Objects and Group Targets using
	Random Matrices}},
  booktitle = {{FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION,
	VOLS 1-4}},
  year = {{2009}},
  pages = {{1029-1036}},
  address = {{345 E 47TH ST, NEW YORK, NY 10017 USA}},
  organization = {{ISIF; IEEE; ONR; ARL; AF Res Lab; Northrop Grumman; CUBRC; Boeing;
	Georgia Tech Res Inst}},
  publisher = {{IEEE}},
  note = {{12th International Conference on Information Fusion, Seattle, WA,
	JUL 06-09, 2009}},
  abstract = {{The task of tracking extended objects or (partly) unresolvable group
	targets raises new challenges for both data association and track
	maintenance. Due to limited sensor resolution capabilities, group
	targets (i.e., a number of closely spaced targets moving in a coordinated
	fashion) may show a similar detection pattern as extended objects,
	namely a varying number of detections whose spread is determined
	by both the statistical sensor errors as well as the physical extension
	of the group or extended object. Different tracking approaches treating
	these situations have been proposed where physical extension is represented
	by a random symmetric positive definite matrix. This paper discusses
	some results that should give deeper insight into behavior and performance
	analysis of these approaches. Further improvements are presented.}},
  affiliation = {{Feldmann, M (Reprint Author), FGAN Res Inst Commun Informat Proc
	\& Ergon FKIE, D-53343 Wachtberg, Germany. {[}Feldmann, Michael]
	FGAN Res Inst Commun Informat Proc \& Ergon FKIE, D-53343 Wachtberg,
	Germany.}},
  book-group-author = {{IEEE}},
  doc-delivery-number = {{BMT89}},
  file = {Feldmann2009.pdf:/home/postgres/doctor/lib/Feldmann2009.pdf:PDF},
  isbn = {{978-0-9824-4380-4}},
  keywords = {{Target tracking; extended targets; group targets; formations; sensor
	resolution; random matrices; matrix-variate analysis}},
  keywords-plus = {{HYPOTHESIS DENSITY FILTER}},
  language = {{English}},
  number-of-cited-references = {{14}},
  owner = {postgres},
  subject-category = {{Computer Science, Artificial Intelligence; Computer Science, Theory
	\& Methods}},
  times-cited = {{0}},
  timestamp = {2010.12.29},
  type = {{Proceedings Paper}},
  unique-id = {{ISI:000273560000133}}
}

@INPROCEEDINGS{Feldmann2009a,
  author = {Feldmann, Michael and Fraenken, Dietrich},
  title = {{Tracking of Extended Objects and Group Targets using Random Matrices
	- A Performance Analysis}},
  booktitle = {{12th International Conference on Information Fusion, Seattle, WA,
	JUL 06-09, 2009}},
  year = {{2009}},
  pages = {{1029-1036}},
  address = {{345 E 47TH ST, NEW YORK, NY 10017 USA}},
  organization = {{ISIF; IEEE; ONR; ARL; AF Res Lab; Northrop Grumman; CUBRC; Boeing;
	Georgia Tech Res Inst}},
  publisher = {{IEEE}},
  abstract = {{The task of tracking extended objects or (partly) unresolvable group
	targets raises new challenges for both data association and track
	maintenance. Due to limited sensor resolution capabilities, group
	targets (i.e., a number of closely spaced targets moving in a coordinated
	fashion) may show a similar detection pattern as extended objects,
	namely a varying number of detections whose spread is determined
	by both the statistical sensor errors as well as the physical extension
	of the group or extended object. Different tracking approaches treating
	these situations have been proposed where physical extension is represented
	by a random symmetric positive definite matrix. This paper discusses
	some results that should give deeper insight into behavior and performance
	analysis of these approaches. Further improvements are presented.}},
  affiliation = {{Feldmann, M (Reprint Author), FGAN Res Inst Commun Informat Proc
	\& Ergon FKIE, D-53343 Wachtberg, Germany. {[}Feldmann, Michael]
	FGAN Res Inst Commun Informat Proc \& Ergon FKIE, D-53343 Wachtberg,
	Germany.}},
  book-group-author = {{IEEE}},
  doc-delivery-number = {{BMT89}},
  file = {Feldmann2009a.pdf:/home/postgres/doctor/lib/Feldmann2009a.pdf:PDF},
  isbn = {{978-0-9824-4380-4}},
  keywords = {{Target tracking; extended targets; group targets; formations; sensor
	resolution; random matrices; matrix-variate analysis}},
  keywords-plus = {{HYPOTHESIS DENSITY FILTER}},
  language = {{English}},
  number-of-cited-references = {{14}},
  owner = {postgres},
  subject-category = {{Computer Science, Artificial Intelligence; Computer Science, Theory
	\& Methods}},
  times-cited = {{0}},
  timestamp = {2010.12.29},
  type = {{Proceedings Paper}},
  unique-id = {{ISI:000273560000133}}
}

@ARTICLE{Feldmann2011,
  author = {Feldmann, Michael and Fraenken, Dietrich and Koch, Wolfgang},
  title = {{Tracking of Extended Objects and Group Targets Using Random Matrices}},
  journal = {{IEEE TRANSACTIONS ON SIGNAL PROCESSING}},
  year = {{2011}},
  volume = {{59}},
  pages = {{1409-1420}},
  number = {{4}},
  month = {{APR}},
  abstract = {{The task of tracking extended objects or ( partly) un-resolvable
	group targets raises new challenges for both data association and
	track maintenance. Due to limited sensor resolution capabilities,
	group targets (i.e., a number of closely spaced targets moving in
	a coordinated fashion) may show a similar detection pattern as extended
	objects, namely a varying number of detections whose spread is determined
	by both the statistical sensor errors as well as the physical extension
	of the group or extended object. In both cases, tracking and data
	association under the ``one target-one detection{''} assumption are
	no longer applicable. This paper deals with the problem of maintaining
	a track for an extended object or group target with varying number
	of detections. Herein, object extension is represented by a symmetric
	positive definite random matrix. A recently published Bayesian approach
	to tackling this problem is analyzed and discussed. From there, a
	new approach is derived that is expected to overcome some of the
	weaknesses the mentioned Bayesian approach suffers from in certain
	applications.}},
  doi = {{10.1109/TSP.2010.2101064}},
  file = {Feldmann2011.pdf:/home/postgres/doctor/lib/Feldmann2011.pdf:PDF},
  issn = {{1053-587X}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000290810100007}}
}

@INPROCEEDINGS{Feldmann2008,
  author = {Feldmann, M. and Franken, D.},
  title = {{Tracking of extended objects and group targets using random matrices
	- a new approach}},
  booktitle = {Information Fusion, 2008 11th International Conference on},
  year = {2008},
  pages = {1 -8},
  month = {30 2008-july 3},
  file = {Feldmann2008.pdf:/home/postgres/doctor/lib/Feldmann2008.pdf:PDF},
  keywords = {data association;extended objects tracking;group targets;object extension;random
	matrices;random symmetric positive definite matrix;track maintenance;Bayes
	methods;matrix algebra;random processes;sensor fusion;target tracking;},
  owner = {postgres},
  timestamp = {2011.06.07}
}

@INPROCEEDINGS{Feldmann2011a,
  author = {Feldmann, M. and Nickel, U. and Koch, W.},
  title = {Adaptive air-to-air target tracking in severe jamming environment},
  booktitle = {Information Fusion (FUSION), 2011 Proceedings of the 14th International
	Conference on},
  year = {2011},
  pages = {1 -8},
  month = {july},
  __markedentry = {[postgres:1]},
  file = {Feldmann2011a.pdf:Feldmann2011a.pdf:PDF},
  keywords = {adaptive air-to-air target tracking;adaptive beamforming;adaptive
	jammer suppression;adaptive phased array radars;angle measurement
	uncertainties;jammer notch;jamming interference;severe jamming environment;track
	continuity;track loss;track stability;adaptive signal processing;array
	signal processing;interference suppression;jamming;phased array radar;radar
	tracking;target tracking;},
  owner = {postgres},
  timestamp = {2012.04.26}
}

@ARTICLE{Friedman2010,
  author = {Kerome Friedman and Trevor Hastie and Rob Tibshirani},
  title = {Regularisation Paths for Generalized Linear Models via Coordinate
	Descent},
  journal = {Journal of Statistic Software},
  year = {2010},
  volume = {33(1)},
  pages = {1--22},
  __markedentry = {[postgres:]},
  file = {Friedman2010.pdf:blasso/Friedman2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@TECHREPORT{Gabriel1989,
  author = {Frenkel Gabriel and E. Fridling Barry},
  title = {Survey of Strategic Defense Initiative Tracking Algorithms},
  institution = {IDA: Institute for Defense Analysis},
  year = {1989},
  file = {:/home/postgres/doctor/lib/gabriel1989.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.01.12}
}

@ELECTRONIC{Gallager2008,
  author = {Robert G. Gallager},
  month = {January},
  year = {2008},
  title = {Circularly--Symmetric Gaussian random vectors},
  language = {English},
  __markedentry = {[postgres:]},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@ARTICLE{Garber2003,
  author = {Jean M. Garber and Arthur C. Williamson},
  title = {{Multi-Mission Maritime Aircraft Survivability in Modern Maritime
	Patrol and Reconnaissance Missions}},
  journal = {{Johns Hopkins APL Technical Digest}},
  year = {2003},
  volume = {24},
  pages = {304--309},
  file = {Garber2003.pdf:Garber2003.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.19}
}

@ARTICLE{Garber2003a,
  author = {Jean M. Garber and Arthur C. Williamson},
  title = {Multi-Mission Maritime Aircraft Survivability in Modern Maritime
	Patrol and Reconnaissance Missions},
  journal = {Johns Hopkins {APL} Technical Digest},
  year = {2003},
  volume = {24 (3)},
  pages = {{304-309}},
  file = {Garber2003.pdf:trad/Garber2003.pdf:PDF},
  owner = {postgres},
  review = {10dB},
  timestamp = {2011.10.25}
}

@INPROCEEDINGS{Gilholm2005,
  author = {Kevin Gilholm and Simon Godsill and Simon Maskell and David Salmond},
  title = {{Poisson models for extended target and group tracking}},
  booktitle = {{SPIE Conerence 5913: Signal and Data Processing of Small Targets
	2005}},
  year = {{2005}},
  editor = {O. E. Drummond},
  month = {August},
  file = {Gilholm2005.pdf:/home/postgres/doctor/lib/Gilholm2005.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.06.07}
}

@ARTICLE{Gorodnitsky1997,
  author = {I. F. Gorodnitsky and Bhaskar D. Rao},
  title = {Sparse signal reconstruction from limited data using FOCUSS: a re--weighted
	minimum norm algorithm},
  journal = {IEEE Transactions on Signal Processing},
  year = {1997},
  volume = {45},
  pages = {600--616},
  __markedentry = {[postgres:1]},
  file = {Gorodnitsky1997.pdf:sbl/Gorodnitsky1997.pdf:PDF},
  owner = {postgres},
  review = {UNREAD, REF ONLY},
  timestamp = {2012.08.14}
}

@INPROCEEDINGS{Hacker2011,
  author = {Hacker, P. and Bin Yang},
  title = {Analytical investigation of two-object DOA estimation},
  booktitle = {Smart Antennas (WSA), 2011 International ITG Workshop on},
  year = {2011},
  pages = {1 -7},
  month = {feb.},
  __markedentry = {[postgres:1]},
  abstract = {Current automotive radar systems measure the distance, the relative
	velocity and the direction of objects in their environment. This
	information enables the car to support the driver. When objects cannot
	be completely separated by their distance or relative velocity, a
	two object direction of arrival estimator must normally be used to
	separate the objects. In this paper we take a close look at the Crame
	#x0301;r-Rao bound of the two-object estimation problem. We show
	the influence of the objects' phase difference and their signal power.
	Furthermore, criterions are introduced, which can be used to optimize
	the average direction estimation performance of an antenna array.
	It turns out that there are array and angle combinations which are
	more sensitive to phase difference changes than others.},
  doi = {10.1109/WSA.2011.5741939},
  file = {Hacker2011.pdf:Xeye/music_ML_CRB/Hacker2011.pdf:PDF},
  keywords = {Cramer-Rao bound;antenna array;automotive radar systems;phase difference;relative
	velocity;two-object DOA estimation;two-object estimation problem;antenna
	arrays;array signal processing;direction-of-arrival estimation;radar
	signal processing;},
  owner = {postgres},
  timestamp = {2012.04.26}
}

@MISC{jdradm0,
  author = {Chris Haddox},
  title = {JDRADM: Backgrounder},
  howpublished = {Boeing},
  month = {February},
  year = {2010},
  file = {jdradm0.pdf:jdradm0.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.15}
}

@TECHREPORT{Hale2007,
  author = {Elaine T. Hale and Wotao Yin and Yin Zhang},
  title = {A Fixed-Point Continuation Method for $\ell_1$-Regularized Minimization
	with Applications to Compressed Sensing},
  institution = {CAAM Technical Report TR07-07, Rice University},
  year = {2007},
  file = {Hale2007.pdf:sbl/Hale2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@MASTERSTHESIS{Hall2010,
  author = {Jacob Thomas Hall},
  title = {Modeling and Analysis of a Maneuvering Aircraft and Cable Towed Body
	with Wake Effects},
  school = {Massachusetts Institute of Technology},
  year = {2010},
  file = {Hall2010.pdf:trad/Hall2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.25}
}

@ARTICLE{Hans2009,
  author = {Chris Hans},
  title = {Bayesian Lasso Regression},
  journal = {Biometrika},
  year = {2009},
  volume = {September},
  pages = {1--11},
  __markedentry = {[postgres:]},
  file = {Hans2009.pdf:blasso/Hans2009.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Hennenfent2008,
  author = {Gilles Hennenfent and Ewout van den Berg and Michael P. Friedlander
	and Felix J. Herrmann},
  title = {New insights into one-norm solvers from the Pareto curve},
  journal = {Geophysics},
  year = {2008},
  volume = {73(4)},
  pages = {23--26},
  __markedentry = {[postgres:]},
  file = {Hennenfent2008.pdf:blasso/Hennenfent2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Hennenfent2008a,
  author = {Gilles Hennenfent and Felix J. Herrmann},
  title = {One-norm regularized inversion: learning from the Pareto curve},
  year = {2009},
  volume = {April},
  pages = {1--10},
  __markedentry = {[postgres:]},
  file = {Hennenfent2008a.pdf:blasso/Hennenfent2008a.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Hosseini2012,
  author = {Bamdad Hosseini and Guoqing Liu and Charles Puelz and Samantha Tracht
	and Mikhail Smilovic},
  title = {Visualizing the Pareto Surface},
  journal = {IMA},
  year = {2012},
  pages = {1--20},
  __markedentry = {[postgres:]},
  file = {Hosseini2012.pdf:blasso/Hosseini2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Hunter2004,
  author = {David R. Hunter and Kenneth Lange},
  title = {A Tutorial on MM Algorithms},
  journal = {The American Statistician},
  year = {2004},
  volume = {58(1)},
  pages = {30--37},
  __markedentry = {[postgres:]},
  file = {Hunter2004.pdf:sbl/Hunter2004.pdf:PDF},
  owner = {postgres},
  review = {注意发掘MM和EM的关系，能说清会描述},
  timestamp = {2012.09.12}
}

@ARTICLE{Hyder2010,
  author = {Md Mashud Hyder and Kaushik Mahata},
  title = {Direction-of-Arrival Estimation Using a Mixed $\ell_{2,0}$ Norm Approximation},
  journal = {IEEE Transactions on Signal Processing},
  year = {2010},
  volume = {58},
  pages = {4646--4655},
  __markedentry = {[postgres:]},
  file = {Hyder2010.pdf:sbl/Hyder2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@ARTICLE{hyvarinen1999fast,
  author = {Hyvarinen, A.},
  title = {Fast and robust fixed-point algorithms for independent component
	analysis},
  journal = {Neural Networks, IEEE Transactions on},
  year = {1999},
  volume = {10},
  pages = {626--634},
  number = {3}
}

@MISC{elm2052,
  author = {{IAI ELTA Systems Ltd.}},
  title = {Active Electronic Scan Array Airborne Fire Control Radar : EL/M--2052},
  howpublished = {{ELTA Target Acquisition and Fire Control}},
  month = {3},
  year = {2009},
  file = {elm2052.pdf:elm2052.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.19}
}

@INPROCEEDINGS{Isoda2010,
  author = {Isoda, K. and Hara, T.},
  title = {Angle measurement method for two targets within antenna beam width
	using two receivers},
  booktitle = {Radar Conference, 2010 IEEE},
  year = {2010},
  pages = {54 -59},
  month = {may},
  abstract = {Monopulse angle measurement methods are often utilized to measure
	a target angle. However, this method cannot measure a correct angle
	for multiple targets which cannot be distinguished by range, Doppler
	frequency and beam width. A method to simultaneously measure azimuths
	and elevations for only two targets was proposed, which requires
	a low computational load compared with other methods. However, the
	conventional method requires four receivers, therefore it is difficult
	to utilize it when there is a hard ware limitation. In this paper,
	we propose a method to measure azimuths and elevations for two targets
	using two receivers and time division system. We also show a verification
	of the proposed method by a numerical simulation and experimentation,
	though measured angles are restricted to azimuths or elevations.
	The results show that two targets angles can be measured by our proposed
	method using two receivers.},
  doi = {10.1109/RADAR.2010.5494654},
  file = {Isoda2010.pdf:Isoda2010.pdf:PDF},
  issn = {1097-5659},
  keywords = {Doppler frequency;antenna beam width;monopulse angle measurement methods;numerical
	simulation;receivers;target angle measurement;time division system;angular
	measurement;antennas;numerical analysis;radio receivers;},
  owner = {postgres},
  timestamp = {2012.03.22}
}

@ELECTRONIC{janes2012,
  author = {Jane's},
  month = {Feb},
  year = {2012},
  title = {MSP-418K RF jamming system (Russian Federation), Airborne electronic
	warfare (EW) systems},
  language = {English},
  url = {http://articles.janes.com/articles/Janes-Avionics/MSP-418K-RF-jamming-system-Russian-Federation.html},
  owner = {postgres},
  timestamp = {2012.04.11}
}

@ELECTRONIC{janes2009,
  author = {Jane's},
  month = {February},
  year = {2009},
  title = {Sorbtsiya radar jammer (Russian Federation), airborne electronic
	warfare (EW) Systems.},
  language = {English},
  organization = {Jane's Avionics},
  url = {http://www.janes.com/articles/Janes-Avionics/Sorbtsiya-radar-jammer-Russian-Federation.html},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@ARTICLE{Ji2008,
  author = {Shihao Ji and Ya Xue and Lawrence Carin},
  title = {Bayesian Compressive Sensing},
  journal = {IEEE Transactions on Signal Processing},
  year = {2008},
  volume = {56},
  pages = {2346--2356},
  __markedentry = {[postgres:1]},
  file = {Ji2008.pdf:sbl/Ji2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.13}
}

@INPROCEEDINGS{Kanevsky2010,
  author = {Kanevsky, D. and Carmi, A. and Horesh, L. and Gurfil, P. and Ramabhadran,
	B. and Sainath, T.N.},
  title = {Kalman filtering for compressed sensing},
  booktitle = {Information Fusion (FUSION), 2010 13th Conference on},
  year = {2010},
  pages = {1 -8},
  month = {july},
  abstract = {Compressed sensing is a new emerging field dealing with the reconstruction
	of a sparse or, more precisely, a compressed representation of a
	signal from a relatively small number of observations, typically
	less than the signal dimension. In our previous work we have shown
	how the Kalman filter can be naturally applied for obtaining an approximate
	Bayesian solution for the compressed sensing problem. The resulting
	algorithm, which was termed CSKF, relies on a pseudo-measurement
	technique for enforcing the sparseness constraint. Our approach raises
	two concerns which are addressed in this paper. The first one refers
	to the validity of our approximation technique. In this regard, we
	provide a rigorous treatment of the CSKF algorithm which is concluded
	with an upper bound on the discrepancy between the exact (in the
	Bayesian sense) and the approximate solutions. The second concern
	refers to the computational overhead associated with the CSKF in
	large scale settings. This problem is alleviated here using an efficient
	measurement update scheme based on Krylov subspace method.},
  file = {Kanevsky2010.pdf:sbl/Kanevsky2010.pdf:PDF},
  keywords = {Bayesian solution;CSKF algorithm;Krylov subspace method;compressed
	sensing problem;kalman filtering;measurement update scheme;pseudomeasurement
	technique;signal compressed representation;Bayes methods;Kalman filters;signal
	representation;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Kelly2008,
  author = {Kevin T. Kelly},
  title = {Ockham's Razor, Hume's Problem, Ellsberg's Paradox, Dilation, and
	Optimal Truth Conduciveness},
  year = {2008},
  __markedentry = {[postgres:]},
  file = {Kelly2008.pdf:sbl/Kelly2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Kerins1993,
  author = {William J. Kerins},
  title = {Analysis of Towed Decoys},
  journal = {IEEE TRANSACTIONS on AEROSPACE and ELECTRONIC SYSTEMS},
  year = {1993},
  volume = {29 (4)},
  pages = {{1222-1227}},
  file = {Kerins1993.pdf:trad/Kerins1993.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.25}
}

@ARTICLE{Kim2007,
  author = {Seung-Jean Kim and K. Koh and M. Lustig and Stephen Boyd and Dimitry
	Gorinevsky},
  title = {An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least
	Squares},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  year = {2007},
  volume = {1},
  pages = {606--617},
  __markedentry = {[postgres:1]},
  file = {Kim2007.pdf:sbl/Kim2007.pdf:PDF},
  owner = {postgres},
  review = {UNREAD, REF ONLY},
  timestamp = {2012.06.08}
}

@ARTICLE{Koch2008,
  author = {Koch, Johann Wolfgang},
  title = {{Bayesian Approach to Extended Object and Cluster Tracking using
	Random Matrices}},
  journal = {{IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS}},
  year = {{2008}},
  volume = {{44}},
  pages = {{1042-1059}},
  number = {{3}},
  month = {{JUL}},
  abstract = {{In algorithms for tracking and sensor data fusion the targets to
	be observed are usually considered as point source objects; i.e.,
	compared with the sensor resolution their extension is neglected.
	Due to the increasing resolution capabilities of modem sensors, however,
	this assumption is often no longer valid as different scattering
	centers of an object can cause distinct detections when passing the
	signal processing chain. Examples of extended targets are found in
	short-range applications (littoral surveillance, autonomous weapons,
	or robotics). A collectively moving target group can also be considered
	as an extended target. This point of view is the more appropriate,
	the smaller the mutual distances between the individual targets are.
	Due to the resulting data association and resolution conflicts any
	attempt of tracking the individual objects within the group seems
	to be no longer reasonable. With simulated sensor data produced by
	a partly unresolvable aircraft formation the addressed phenomena
	are illustrated and an approximate Bayesian solution to the resulting
	tracking problem is proposed. Ellipsoidal object extensions are modeled
	by random matrices, which are treated as additional state variables
	to be estimated or tracked. We expect that the resulting tracking
	algorithms are also relevant for tracking large, collectively moving
	target swarms.}},
  address = {{445 HOES LANE, PISCATAWAY, NJ 08855 USA}},
  affiliation = {{Koch, JW (Reprint Author), FGAN FKIE, Neuenahrer Str 20, D-53343
	Wachtberg, Germany. FGAN FKIE, D-53343 Wachtberg, Germany.}},
  author-email = {{w.koch@fgan.de}},
  cited-references = {{BARSHALOM Y, 2001, ESTIMATION APPL TRAC. BLACKMAN S, 1999, DESIGN
	ANAL MODERN T. BLASCH E, 2001, P WORKSH EST TRACK F, P360. BLOM HAP,
	IEEE T AERO IN PRESS. CONNARE T, 2001, P WORKSH EST TRACK F, P205.
	DAUM FE, 1994, P SOC PHOTO-OPT INS, V2235, P329. DEZERT J, 1998,
	P SOC PHOTO-OPT INS, V3373, P83. DRUMMOND O, 1990, P SOC PHOTO-OPT
	INS, V1305, P362. DRUMMOND OE, 1997, P SOC PHOTO-OPT INS, V3163,
	P249. GORDON NJ, 2001, SEQUENTIAL MONTE CAR. GUPTA AK, 1999, MATRIX
	VARIATE DISTR. HARVILLE DA, 1997, MATRIX ALGEBRA STAT. KOCH W, 1997,
	IEEE T AERO ELEC SYS, V33, P883. KOCH W, 2001, ADV SIGNAL PROCESSIN,
	CH7. KOCH W, 2004, P 5 S INT AUT VEH LI. MAGNUS JR, 1999, MATRIX
	DIFFERENTIAL. MAHLER RPS, 2003, IEEE T AERO ELEC SYS, V39, P1152.
	MAHLER RPS, 2004, IEEE AERO EL SYS M 2, V19, P53. RISTIC B, 2004,
	KALMAN FILTERING. SALMOND DJ, 1999, IEE C TARG TRACK. SALMOND DJ,
	2003, IEE P RAD SON NAV DE, V150. SAUL R, 2005, SDF20052 FKIE. ULMKE
	M, 2007, P FUSION 2007 QUEB C. VANKEUK G, 1993, IEEE T AERO ELEC
	SYS, V29, P186. VO BN, 2005, IEEE T AERO ELEC SYS, V41, P1224. WAXMAN
	MJ, 2004, P SOC PHOTO-OPT INS, V5428, P551, DOI 10.1117/12.548357.
	WIGNER EP, 1967, SIAM REV, V9, P1.}},
  doc-delivery-number = {{364JM}},
  file = {:/home/postgres/doctor/lib/koch2008.pdf:PDF},
  issn = {{0018-9251}},
  journal-iso = {{IEEE Trans. Aerosp. Electron. Syst.}},
  keywords-plus = {{TARGET TRACKING}},
  language = {{English}},
  number-of-cited-references = {{27}},
  owner = {postgres},
  publisher = {{IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC}},
  subject-category = {{Engineering, Aerospace; Engineering, Electrical \& Electronic; Telecommunications}},
  times-cited = {{6}},
  timestamp = {2010.12.29},
  type = {{Article}},
  unique-id = {{ISI:000260332000016}}
}

@ARTICLE{Koch2010,
  author = {Koch, Wolfgang},
  title = {{On Bayesian Tracking and Data Fusion: A Tutorial Introduction with
	Examples}},
  journal = {{IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE}},
  year = {{2010}},
  volume = {{25}},
  pages = {{29-51}},
  number = {{7, Part 2}},
  month = {{JUL}},
  abstract = {{This tutorial paper provides a short introduction to selected aspects
	of sensor data fusion by discussing characteristic examples. We consider
	three cases when fusion of sensor data is important: when emphasis
	is placed on data produced at different instants of time (i.e., target
	tracking), when data being collected from different sensor sources
	are important, and when we have data with background information
	on the sensor performance as well as data with nonsensor context
	information. The feedback from data processing to the data acquisition
	process is illustrated by a sensor management example.}},
  doi = {{10.1109/MAES.2010.5546307}},
  file = {Koch2010.pdf:/home/postgres/doctor/lib/Koch2010.pdf:PDF},
  issn = {{0885-8985}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000283377200002}}
}

@INPROCEEDINGS{Koch2009,
  author = {Koch, Wolfgang},
  title = {{On Accumulated State Densities with Applications to Out-of-Sequence
	Measurement Processing}},
  booktitle = {{FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION,
	VOLS 1-4}},
  year = {{2009}},
  pages = {{2201-2208}},
  note = {{12th International Conference on Information Fusion, Seattle, WA,
	JUL 06-09, 2009}},
  abstract = {{In tat-get tracking applications, the full information on the kinematic
	target states accumulated over a certain time window up to the present
	time is contained in the joint probability density function of these
	state vectors, given the time series of all sensor data. This joint
	density may also be called an Accumulated State Density (ASD) and
	provides a unified treatment of filtering and retrodiction insofar
	as by marginalizing the ASD, the standard filtering and retrodiction
	densities are obtained. In addition, ASDs fully describe the correlations
	between the state estimates produced for different instants of time.
	The paper discusses the notion of ASDs and closed formulae for calculating
	them. The practical usefulness of considering ASDs is illustrated
	by applications, where out-of-sequence (OoS) measurements are to
	be processed within the framework of a centralized measurement fusion
	architecture, i.e. when the sensor data do not arrive in the temporal
	order in which they were produced. The approach can be applied to
	Kalman-, MHT-, and IMM filtering.}},
  book-group-author = {{IEEE}},
  file = {Koch2009.pdf:/home/postgres/doctor/lib/Koch2009.pdf:PDF},
  isbn = {{978-0-9824-4380-4}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000273560001139}}
}

@ARTICLE{Koch2009a,
  author = {Koch, Wolfgang and Feldmann, Michael},
  title = {{Cluster tracking under kinematical constraints using random matrices}},
  journal = {{ROBOTICS AND AUTONOMOUS SYSTEMS}},
  year = {{2009}},
  volume = {{57}},
  pages = {{296-309}},
  number = {{3, Sp. Iss. SI}},
  month = {{MAR 31}},
  note = {{IEEE International Conference on Multisensor Fusion and Integration,
	Heidelberg, GERMANY, SEP 03-06, 2006}},
  abstract = {{Collectively moving object clusters are of particular interest in
	certain applications and have to be tracked as separate aggregated
	entities consisting of an unknown number of individuals. Group targets
	often will not cause as many detections since there are individual
	targets in the group due to limited sensor resolution capabilities.
	In this case, tracking and data association under the `one target,
	one detection' assumption are no longer applicable. In this paper,
	ellipsoidal object extensions are modeled by random matrices. which
	are treated as additional state variables to be estimated. An important
	aspect of GMT{[} tracking is the incorporation of context information
	into the Bayesian data processing formalism. Here we consider kinematical
	constraints such as road maps and sensor specific characteristics
	such as Doppler blindness. (C) 2008 Elsevier B.V. All rights reserved.}},
  doi = {{10.1016/j.robot.2008.10.006}},
  file = {Koch2009a.pdf:/home/postgres/doctor/lib/Koch2009a.pdf:PDF},
  issn = {{0921-8890}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000264209000008}}
}

@INPROCEEDINGS{Koch2005,
  author = {Koch, W and Saul, R},
  title = {{A Bayesian approach to extended object tracking and tracking of
	loosely structured target groups}},
  booktitle = {{2005 7th International Conference on Information Fusion (FUSION),
	Vols 1 and 2}},
  year = {{2005}},
  pages = {{827-834}},
  note = {{7th International Conference on Information Fusion (FUSION), Philadelphia,
	PA, JUL 25-28, 2005}},
  abstract = {{In algorithms for tracking and sensor datafusion the targets to be
	tracked are usually considered as point source objects; i.e. compared
	to the sensor resolution their extension is neglected Due to the
	increasing resolution capabilities of modem sensors, however this
	assumption is often not valid: Different scattering centers of an
	object can cause distinct detections when passing the signal processing
	chain. Examples of extended targets are found in short-range applications
	(littoral surveillance, autonomous weapons, or robotics). As an extended
	target also a collectively moving, loosely structured group can be
	considered. This point of view is all the more appropriate, the smaller
	the mutual distances between the individual targets are. Due to the
	resulting data association and resolution conflicts any attempt of
	tracking the individual objects is no longer reasonable. With simulated
	sensor data produced by a partly resolvable aircraft formation the
	addressed phenomena are illustrated and a Bayesian solution to the
	resulting tracking problem is proposed. Ellipsoidal object extensions
	are modeled by random matrices and treated as additional state variables
	to be estimated or `tracked'. We expect that the resulting tracking
	algorithms are relevant also for tracking large, collectively moving
	target swarms.}},
  book-group-author = {{IEEE}},
  file = {Koch2005.pdf:/home/postgres/doctor/lib/Koch2005.pdf:PDF},
  isbn = {{0-7803-9286-8}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000234830400111}}
}

@ARTICLE{Koller-Meier2001,
  author = {Koller-Meier, EB and Ade, F},
  title = {{Tracking multiple objects using the Condensation algorithm}},
  journal = {{ROBOTICS AND AUTONOMOUS SYSTEMS}},
  year = {{2001}},
  volume = {{34}},
  pages = {{93-105}},
  number = {{2-3}},
  month = {{FEB 28}},
  note = {{EUROBOT 99 Workshop, ZURICH, SWITZERLAND, SEP 06-08, 1999}},
  abstract = {{Some years ago a new tracker, the Condensation algorithm, came to
	be known in the computer vision community. It describes a stochastic
	approach that has neither restrictions on the system and measurement
	models used nor on the distributions of the error sources, but it
	cannot track an arbitrary changing number of objects. In this paper
	an extension of the Condensation algorithm is introduced that relies
	on a single probability distribution to describe the likely states
	of multiple objects. By introducing an initialization density, observations
	can flow directly into the tracking process, such that newly appearing
	objects can be handled. (C) 2001 Elsevier Science B.V. All rights
	reserved.}},
  file = {Koller-Meier2001.pdf:/home/postgres/doctor/lib/Koller-Meier2001.pdf:PDF},
  issn = {{0921-8890}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000168297700004}}
}

@ELECTRONIC{aus2008a,
  author = {Dr Carlo Kopp},
  month = {March},
  year = {2008},
  title = {The Russian Philosophy of Beyond Visual Range Air Combat},
  language = {English},
  organization = {Air Power Australia},
  url = {http://www.ausairpower.net/APA-Rus-BVR-AAM.html},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@TECHREPORT{Lange2007,
  author = {Kenneth Lange},
  title = {The MM Algorithm},
  institution = {Departments of Biomathematics, Hunman Genetics, and Statistics},
  year = {2007},
  __markedentry = {[postgres:]},
  file = {Lange2007.pdf:sbl/Lange2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.12}
}

@ARTICLE{Lewicki2000,
  author = {Michael Lewicki and Terrence J. Sejnowski},
  title = {Learning Overcomplete Representations},
  journal = {Neural Computation},
  year = {2000},
  volume = {12},
  pages = {337--365},
  __markedentry = {[postgres:1]},
  file = {Lewicki2000.pdf:sbl/Lewicki2000.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Lian2010,
  author = {Lian, F. and Han, C. -Z. and Liu, W. -F. and Yan, X. -X. and Zhou,
	H. -Y.},
  title = {{Sequential Monte Carlo implementation and state extraction of the
	group probability hypothesis density filter for partly unresolvable
	group targets-tracking problem}},
  journal = {{IET RADAR SONAR AND NAVIGATION}},
  year = {{2010}},
  volume = {{4}},
  pages = {{685-702}},
  number = {{5}},
  month = {{OCT}},
  abstract = {{This study proposes a sequential Monte Carlo (SMC) implementation
	to the Mahler's group probability hypothesis density filter (PHDF)
	for partly unresolvable group targets tracking problem. A potential
	limitation of the group PHDF is that it cannot be used to determine
	the number of groups. Therefore we have to jointly extract the group
	number and states from the proposed group SMC-PHDF at each time step.
	We propose to fit the resampled particles of the group SMC-PHDF by
	application of Gaussian mixture models with unknown component number.
	In the mixture, the number and parameters of the components correspond
	to the number and states of the groups over the observation region.
	The Markov chain Monte Carlo (MCMC) algorithm is proposed to estimate
	the component parameters of the mixture. The estimate of component
	number of the mixture can be derived by a component management strategy.
	In simulation, the proposed group SMC-PHDF with the expectation maximum
	(EM) and MCMC extractions are, respectively, used to detect and track
	the groups. Hundred Monte Carlo simulation results show that the
	latter outperforms the former a lot in estimating the group number
	and states, although the computational requirement of the MCMC extraction
	is more expensive than the EM extraction.}},
  doi = {{10.1049/iet-rsn.2009.0109}},
  file = {Lian2010.pdf:/home/postgres/doctor/lib/Lian2010.pdf:PDF},
  issn = {{1751-8784}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000281351200004}}
}

@INPROCEEDINGS{Liu2009,
  author = {Liu, Yuankui and Fan, Yangyu and Zhao, Jiong},
  title = {{Maneuvering Target Tracking in the Case of Wakes}},
  booktitle = {{PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND
	SIGNAL PROCESSING, VOLS 1-9}},
  year = {{2009}},
  editor = {{Qiu, PH and Yiu, C and Zhang, H and Wen, XB}},
  pages = {{4950-4953}},
  note = {{2nd International Congress on Image and Signal Processing, Tianjin,
	PEOPLES R CHINA, OCT 17-19, 2009}},
  abstract = {{An interacting multiple model probability data association method
	for tracking the wake target is presented. When the detections are
	fed to a tracking system like the Probabilistic Data Association
	Filter, the estimated track can be misled and sometimes lose the
	real target because of the wake. This problem becomes even more severe
	when the target performs maneuvering in the presence of wakes. To
	prevent this, we propose a probabilistic model of the wakes and deduce
	the new probability association coefficient, for the targets maneuvering,
	the Interacting Multiple Model is applied. Simulation result shows,
	compared with the Interacting Multiple Model Probabilistic Data Association
	method, the new method could reduce the target loss effectively and
	retain low target loss ratio with augmenting measurement noise.}},
  file = {Liu2009.pdf:/home/postgres/doctor/lib/Liu2009.pdf:PDF},
  isbn = {{978-1-4244-4130-3}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000280804302356}}
}

@INPROCEEDINGS{Lundquist2009,
  author = {Lundquist, Christian and Orguner, Umut and Schon, Thomas B.},
  title = {{Tracking Stationary Extended Objects for Road Mapping using Radar
	Measurements}},
  booktitle = {{2009 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1 AND 2}},
  year = {{2009}},
  series = {{IEEE Intelligent Vehicles Symposium}},
  pages = {{405-410}},
  note = {{IEEE Intelligent Vehicles Symposium, Xian, PEOPLES R CHINA, JUN
	03-05, 2009}},
  abstract = {{It is getting more common that premium cars are equipped with a forward
	looking radar and a forward looking camera. The data is often used
	to estimate the road geometry, tracking leading vehicles, etc. However,
	there is valuable information present in the radar concerning stationary
	objects, that is typically not used. The present work shows how stationary
	objects, such as guard rails, can be modeled and tracked as extended
	objects using radar measurements. The problem is cast within a standard
	sensor fusion framework utilizing the Kalman filter. The approach
	has been evaluated on real data from highways and rural roads in
	Sweden.}},
  book-group-author = {{IEEE}},
  file = {Lundquist2009.pdf:/home/postgres/doctor/lib/Lundquist2009.pdf:PDF},
  isbn = {{978-1-4244-3503-6}},
  issn = {{1931-0587}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000270718200070}}
}

@ARTICLE{MacKay1992,
  author = {David J. C. MacKay},
  title = {Bayesian Interpolation},
  journal = {Neural Computation},
  year = {1992},
  volume = {4(3)},
  pages = {415--447},
  __markedentry = {[postgres:1]},
  file = {MacKay1992.pdf:sbl/MacKay1992.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@INPROCEEDINGS{Mahler2003,
  author = {Mahler, R},
  title = {{Bayesian cluster detection and tracking using a generalized Cheeseman
	approach}},
  booktitle = {{SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XII}},
  year = {{2003}},
  editor = {{Kadar, I}},
  volume = {{5096}},
  series = {{PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
	(SPIE)}},
  pages = {{334-345}},
  note = {{Conference on Signal Processing, Sensor Fusion, and Target Recognition
	XII, ORLANDO, FL, APR 21-23, 2003}},
  abstract = {{Cluster tracking is the problem of detecting and tracking clustered
	formations of large numbers of targets, without necessarily being
	obligated to track each and every individual target. We address this
	problem by generalizing to the dynamic case a static Bayesian finite-mixture
	data-clustering approach due to P. Cheeseman. After summarizing Cheeseman's
	approach, we show that it implicitly draws on random set theory.
	Making this connection explicit allows us to incorporate it into
	a multitarget recursive Bayes filter, thereby leading to a rigorous
	Bayesian foundation for finite-mixture cluster tracking. A computational
	approach is proposed, based on an approximate, multitarget first-order
	moment filter ({''}cluster PHD{''} filter).}},
  isbn = {{0-8194-4956-3}},
  issn = {{0277-786X}},
  owner = {postgres},
  timestamp = {2011.01.06},
  unique-id = {{ISI:000185646200031}}
}

@BOOK{Mahler2007,
  title = {Statistical Multisource-Multitarget Information Fusion},
  publisher = {Artech House},
  year = {2007},
  author = {Ronald P. S. Mahler},
  __markedentry = {[postgres:]},
  file = {Mahler2007.pdf:sbl/Mahler2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.06}
}

@ARTICLE{Malioutov2005,
  author = {Dmitry Malioutov and Mujdat Cetin and Alan S. Willsky},
  title = {A Sparse Signal Reconstruction Perspective for Source Localization
	With Sensor Arrays},
  journal = {IEEE Transactions on Signal Processing},
  year = {2005},
  volume = {53},
  pages = {3010--3022},
  __markedentry = {[postgres:]},
  file = {Malioutov2005.pdf:sbl/Malioutov2005.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@ELECTRONIC{markov2010,
  author = {David R. Markov and Andrew W. Hull},
  month = {March},
  year = {2010},
  title = {Russian Federation's 5th Generation Fighter: PAK--FA(T--50) Program},
  file = {markov2010.pdf:markov2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.21}
}

@ELECTRONIC{mbda0,
  author = {MBDA},
  month = {June},
  year = {2011},
  title = {MBDA Weapons for F--35/JSF},
  howpublished = {MBDA Missile Systems},
  organization = {MBDA},
  file = {mbda0.pdf:mbda0.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.21}
}

@ELECTRONIC{mbda1,
  author = {MBDA},
  month = {July},
  year = {2011},
  title = {METEOR: Beyond Visual Range Air--to--Air Missile(BVRAAM)},
  howpublished = {MBDA},
  organization = {MBDA Missile Systems},
  file = {mbda1.pdf:mbda1.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.21}
}

@ELECTRONIC{merchant2011,
  author = {Kenneth D. Merchant and Mai Gen},
  month = {October},
  year = {2011},
  title = {5th Generation Air Armament},
  howpublished = {37th Armament Symposium},
  organization = {USAF Program Executive Officer for Weapons Commander, Air Armament
	Center},
  file = {merchant2011.pdf:merchant2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.21}
}

@INPROCEEDINGS{Mertens2009,
  author = {Mertens, Michael and Ulmke, Martin and Klemm, Richard and Koch, Wolfgang},
  title = {{Using Lateral Length Measurements in GMTI Convoy Tracking}},
  booktitle = {{FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION,
	VOLS 1-4}},
  year = {{2009}},
  pages = {{1022-1028}},
  note = {{12th International Conference on Information Fusion, Seattle, WA,
	JUL 06-09, 2009}},
  abstract = {{This contribution deals with tracking of convoys moving on ground
	by airborne GMTI radar A technique based on generalized power estimators
	using multiple beams is used to estimate the lateral length component
	of an unresolved convoy. In combination with the conventional range
	measurement, the full length information can be obtained in case
	of a linear convoy. The main focus of this paper is the exploitation
	of this attribute information in order to improve the tracking process.
	The Bayes algorithm based on our earlier work is improved and incorporated
	into a JPDAF framework to deal with multiple targets. The processing
	of Doppler measurements is also included. Numerical results are presented
	based on a simulation scenario.}},
  book-group-author = {{IEEE}},
  file = {Mertens2009.pdf:/home/postgres/doctor/lib/Mertens2009.pdf:PDF},
  isbn = {{978-0-9824-4380-4}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000273560000132}}
}

@ARTICLE{Mohamed2012,
  author = {Shakir Mohamed and Katherine A. Heller and Zoubin Ghahramani},
  title = {Bayesian and $L_1$ Approaches for Sparse Unsupervised Learning},
  journal = {ICML2012},
  year = {2012},
  volume = {1},
  pages = {1--8},
  __markedentry = {[postgres:]},
  file = {Mohamed2012.pdf:blasso/Mohamed2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Morelande2007,
  author = {Mark R. Morelande and Christopher M. Kreucher and Keith Kastella},
  title = {A Bayesian Approach to Multiple Target Detection and Tracking},
  journal = {IEEE Transactions on Signal Processing},
  year = {{2007}},
  volume = {{55}},
  pages = {{1589-1604}},
  file = {Morelande2007.pdf:/home/postgres/doctor/lib/Morelande2007.pdf:PDF},
  language = {En},
  owner = {postgres},
  timestamp = {2011.06.07}
}

@ARTICLE{Mutshinda2010,
  author = {Crispin M. Mutshinda and Mikko J. Sillanpaa},
  title = {Extended Bayesian Lasso for Multiple Quantitative Trait Loci Mapping
	and Unobserved Phenotype Prediction},
  journal = {Genetics Society of America},
  year = {2010},
  volume = {1},
  pages = {1--9},
  __markedentry = {[postgres:]},
  file = {Mutshinda2010.pdf:blasso/Mutshinda2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Nickel2006,
  author = {Nickel, U.},
  title = {Overview of generalized monopulse estimation},
  journal = {Aerospace and Electronic Systems Magazine, IEEE},
  year = {2006},
  volume = {21},
  pages = {27 -56},
  number = {6},
  month = {june },
  __markedentry = {[postgres:1]},
  abstract = {Monopulse is an established technique for radar angle estimation.
	One can show that monopulse estimation is based on a general approximation
	derived from maximum likelihood (ML) estimation. This tutorial provides
	a derivation of this relation and presents extensions of this monopulse
	principle to multi-dimensional array and parameter estimation problems,
	in particular to space-time adaptive processing (STAP) with reduced
	dimension, subarrays and generalized sidelobe canceller (GSLC) configurations.
	The performance of these monopulse applications can be predicted
	by exploiting the distribution of the monopulse ratio. It is demonstrated
	that this distribution is more realistic than the Cramer-Rao bound
	(CRB). Several examples of performance of monopulse estimators are
	given for thinned and fully filled planar arrays, adaptive beamforming
	with and without low sidelobes, GSLC, and STAP. Finally, conditions
	for estimates with low variance are discussed},
  doi = {10.1109/MAES.2006.1662039},
  file = {Nickel2006.pdf:Xeye/music_ML_CRB/Nickel2006.pdf:PDF},
  issn = {0885-8985},
  keywords = {GSLC;STAP;adaptive beamforming;general approximation;generalized monopulse
	estimation;generalized sidelobe canceller;maximum likelihood estimation;monopulse
	principle;multi-dimensional array;parameter estimation problems;planar
	array;radar angle estimation;space-time adaptive processing;subarrays;array
	signal processing;maximum likelihood estimation;multidimensional
	signal processing;radar signal processing;space-time adaptive processing;},
  owner = {postgres},
  timestamp = {2012.04.26}
}

@ARTICLE{Nickel1996,
  author = {U. Nickel},
  title = {Monopulse estimation with subarray-adaptive arrays and arbitary sum
	and difference beams},
  journal = {IET Proc.-Radar, Sonar Navig.},
  year = {1996},
  volume = {143},
  pages = {232--238},
  __markedentry = {[postgres:1]},
  file = {Nickel1996.pdf:Xeye/music_ML_CRB/Nickel1996.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.05.10}
}

@ARTICLE{Park2008,
  author = {Trevor Park and George Casella},
  title = {The Bayesian Lasso},
  year = {2008},
  volume = {1},
  pages = {1--17},
  __markedentry = {[postgres:]},
  file = {Park2008.pdf:blasso/Park2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ELECTRONIC{darpa2009,
  author = {{PB 2010 Defense Advanced Research Projects Agency}},
  month = {May},
  year = {2009},
  title = {0400--Research, Developments, Test \& Evaluation, Defense--Wide/BA--3
	-- Advanced Technology Developments(ATD)},
  howpublished = {RDT \& E Budget item Justification},
  organization = {DARPA},
  file = {darpa2009.pdf:darpa2009.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.22}
}

@ELECTRONIC{Petersen2008,
  author = {Kaare Brandt Petersen and Michael Syskind Pedersen},
  month = {Nov},
  year = {2008},
  title = {The Matrix Cookbook},
  language = {English},
  url = {http://matrixcookbook.com},
  __markedentry = {[postgres:]},
  file = {Petersen2008.pdf:sbl/Petersen2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.06}
}

@ARTICLE{Qian2012,
  author = {Wei QIan and Yuhong Yang},
  title = {Model Selection via Standard error adjusted adaptive lasso},
  journal = {Ann Inst Stat Math},
  year = {2012},
  volume = {1},
  pages = {1--24},
  __markedentry = {[postgres:]},
  file = {Qian2012.pdf:blasso/Qian2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@INPROCEEDINGS{Qiu2009,
  author = {Chenlu Qiu and Wei Lu and Vaswani, N.},
  title = {Real-time dynamic MR image reconstruction using Kalman Filtered Compressed
	Sensing},
  booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE
	International Conference on},
  year = {2009},
  pages = {393 -396},
  month = {april},
  abstract = {In recent work, Kalman Filtered Compressed Sensing (KF-CS) was proposed
	to causally reconstruct time sequences of sparse signals, from a
	limited number of ldquoincoherentrdquo measurements. In this work,
	we develop the KF-CS idea for causal reconstruction of medical image
	sequences from MR data. This is the first real application of KF-CS
	and is considerably more difficult than simulation data for a number
	of reasons, for example, the measurement matrix for MR is not as
	ldquoincoherentrdquo and the images are only compressible (not sparse).
	Greatly improved reconstruction results (as compared to CS and its
	recent modifications) on reconstructing cardiac and brain image sequences
	from dynamic MR data are shown.},
  doi = {10.1109/ICASSP.2009.4959603},
  file = {Qiu2009.pdf:sbl/Qiu2009.pdf:PDF},
  issn = {1520-6149},
  keywords = {Kalman filtered compressed sensing;MR image reconstruction;medical
	images;sparse signals;Kalman filters;biomedical MRI;image reconstruction;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Qiu2010,
  author = {K. Qiu and A. Dogandzic},
  title = {Variance-component based sparse signal reconstruction and model selection},
  journal = {IEEE Transactions on Signal Processing},
  year = {2010},
  volume = {58(6)},
  pages = {2935--2952},
  __markedentry = {[postgres:1]},
  file = {Qiu2010.pdf:sbl/Qiu2010.pdf:PDF},
  owner = {postgres},
  review = {UNREAD, REF ONLY},
  timestamp = {2012.08.18}
}

@PHDTHESIS{Randleff2007,
  author = {Lars Rosenberg Randleff},
  title = {Decision Support Systems Fot Fighter Pilots},
  school = {Technical University of Denmark},
  year = {2007},
  file = {Randleff2007.pdf:trad/Randleff2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.25}
}

@MISC{amraam0,
  author = {Raytheon},
  title = {{AMRAAM:} Advanced Medium--Range Air--to--Air Missile},
  howpublished = {Raytheon Production Data Sheet},
  month = {8},
  year = {2008},
  file = {amraam0.pdf:amraam0.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.15}
}

@BOOK{fisher2008,
  title = {PLA Air Force Equipment Trends},
  year = {2008},
  editor = {Richard D. Fisher, Jr.},
  author = {Richard D. Fisher, Jr.},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@ELECTRONIC{Roweis1999,
  author = {Sam Roweis},
  month = {June},
  year = {1999},
  title = {Matrix Identities},
  language = {English},
  __markedentry = {[postgres:]},
  file = {Roweis1999.pdf:sbl/Roweis1999.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.06}
}

@ARTICLE{rusi2012,
  author = {Rusi},
  title = {Electronic Dragons -- Airborne Electronic Warfare Capabilities in
	China},
  journal = {Rusi Defence Systems},
  year = {2012},
  volume = {14},
  pages = {80--81},
  month = {Spring},
  language = {English},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@ARTICLE{saw2009,
  author = {David Saw},
  title = {A world of complications: The Airborne Electronic Warfare Environment
	in ASIA},
  journal = {Asian Defence \& Diplomacy},
  year = {2009},
  volume = {16},
  pages = {21--28},
  month = {june/july},
  owner = {postgres},
  timestamp = {2012.04.09}
}

@ARTICLE{Seeger2010,
  author = {Matthias W. Seeger and David P. Wipf},
  title = {Variational Bayesian Inference Techniques},
  journal = {IEEE Signal Processing Magazine},
  year = {2010},
  volume = {November},
  pages = {81--91},
  __markedentry = {[postgres:]},
  file = {Seeger2010.pdf:sbl/Seeger2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.17}
}

@ARTICLE{Sharenson1962,
  author = {S. Sharenson},
  title = {Angle Estimation Accuracy with a Monopulse Radar in the Search Mode.},
  journal = {IRE Transactions on Aeospace and Navigational Electronics},
  year = {1962},
  volume = {September},
  pages = {175--179},
  file = {Sharenson1962.pdf:Sharenson1962.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.10}
}

@ARTICLE{Shimamura2006,
  author = {Teppei Shimamura and Hiroyuki Minami and Masahiro Mizuta},
  title = {Regularization Parameter Selection in the Group Lasso},
  year = {2006},
  pages = {1--7},
  __markedentry = {[postgres:]},
  file = {Shimamura2006.pdf:blasso/Shimamura2006.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Shutin2011,
  author = {Dmitriy Shutin and Thomas Bachgraber and Sanjeev R. Kulkarni and
	H. Vincent Poor},
  title = {Fast Variational Sparse Bayesian Learning With Automatic Relevance
	Determination for Superimposed Signals},
  journal = {IEEE Transaction on Signal Processing},
  year = {2011},
  volume = {59(12)},
  pages = {6257--6261}
}

@ARTICLE{Shutin2012,
  author = {Dmitriy Shutin and Sanjeev R. Kulkarni and H. Vincent Poor},
  title = {Incremental Reformulated Automatic Relevance Determination},
  journal = {IEEE Transactions on Signal Processing},
  year = {2012},
  volume = {60(9)},
  pages = {4977--4981},
  __markedentry = {[postgres:1]},
  file = {Shutin2012.pdf:sbl/Shutin2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.13}
}

@ARTICLE{Stoica1989,
  author = {Stoica, P. and Nehorai Arye},
  title = {MUSIC, maximum likelihood, and Cramer-Rao bound},
  journal = {Acoustics, Speech and Signal Processing, IEEE Transactions on},
  year = {1989},
  volume = {37},
  pages = {720 -741},
  number = {5},
  month = {may},
  __markedentry = {[postgres:1]},
  abstract = {The performance of the MUSIC and ML methods is studied, and their
	statistical efficiency is analyzed. The Cramer-Rao bound (CRB) for
	the estimation problems is derived, and some useful properties of
	the CRB covariance matrix are established. The relationship between
	the MUSIC and ML estimators is investigated as well. A numerical
	study is reported of the statistical efficiency of the MUSIC estimator
	for the problem of finding the directions of two plane waves using
	a uniform linear array. An exact description of the results is included},
  doi = {10.1109/29.17564},
  file = {Stoica1989.pdf:Xeye/music_ML_CRB/Stoica1989.pdf:PDF},
  issn = {0096-3518},
  keywords = {Cramer-Rao bound;MUSIC estimator;covariance matrix;direction finding;maximum
	likelihood method;plane waves;signal processing;statistical efficiency;uniform
	linear array;radio direction-finding;signal processing;},
  owner = {postgres},
  timestamp = {2012.04.26}
}

@ARTICLE{Stoica1990,
  author = {Stoica, P. and Nehorai, A.},
  title = {MUSIC, maximum likelihood, and Cramer-Rao bound: further results
	and comparisons},
  journal = {Acoustics, Speech and Signal Processing, IEEE Transactions on},
  year = {1990},
  volume = {38},
  pages = {2140 -2150},
  number = {12},
  month = {dec},
  __markedentry = {[postgres:1]},
  abstract = {The problem of determining the direction-of-arrival of narrowband
	plane waves using sensor arrays and the related problem of estimating
	the parameters of superimposed signals from noisy measurements are
	studied. A number of results have been recently presented by the
	authors on the statistical performance of the multiple signal characterization
	(MUSIC) and the maximum likelihood (ML) estimators for the above
	problems. This work extends those results in several directions.
	First, it establishes that in the class of weighted MUSIC estimators,
	the unweighted MUSIC achieves the best performance (i.e. the minimum
	variance of estimation errors), in large samples. Next, it derives
	the covariance matrix of the ML estimator and presents detailed analytic
	studies of the statistical efficiency of MUSIC and ML estimators.
	These studies include performance comparisons of MUSIC and MLE with
	each other, as well as with the ultimate performance corresponding
	to the Cramer-Rao bound. Finally, some numerical examples are given
	which provide a more quantitative study of performance for the problem
	of finding two directions with uniform linear sensor arrays},
  doi = {10.1109/29.61541},
  file = {Stoica1990.pdf:Xeye/music_ML_CRB/Stoica1990.pdf:PDF},
  issn = {0096-3518},
  keywords = {Cramer-Rao bound;covariance matrix;direction-of-arrival;maximum likelihood
	estimator;multiple signal characterization;narrowband plane waves;noisy
	measurements;parameter estimation;performance comparisons;statistical
	performance;superimposed signals;uniform linear sensor arrays;unweighted
	MUSIC estimator;weighted MUSIC estimators;parameter estimation;signal
	detection;signal processing;statistical analysis;},
  owner = {postgres},
  timestamp = {2012.04.26}
}

@ARTICLE{Stoica1990a,
  author = {Stoica, P. and Sharman, K.C.},
  title = {Maximum likelihood methods for direction-of-arrival estimation},
  journal = {Acoustics, Speech and Signal Processing, IEEE Transactions on},
  year = {1990},
  volume = {38},
  pages = {1132 -1143},
  number = {7},
  month = {jul},
  __markedentry = {[postgres:1]},
  abstract = {Five methods of direction-of-arrival (DOA) estimation which can be
	derived from the maximum-likelihood (ML) principle are considered.
	The ML method (MLM) results from the application of the ML principle
	to the statistics of the observed raw data. The standard multiple
	signal classification (MUSIC) procedure, called MUSIC-1, is obtained
	as a brute-force approximation of the MLM. An improved MUSIC procedure,
	named MUSIC-2, is obtained by applying the ML principle to the statistics
	of certain linear combinations of the sample noise space eigenvectors.
	A procedure which compromises between the good performance of the
	MLM and the computational simplicity of MUSIC is a method of direction
	estimation (MODE-1) which is derived as a large sample realization
	of the MLM. A fifth method, called MODE-2, is obtained by using the
	ML principle on the statistics of certain linear combinations of
	the sample eigenvectors. MODE-2 is computationally less demanding
	than the MLM (it is of the same complexity as MODE-1) and statistically
	more efficient. A numerical comparison of these five DOA estimation
	methods is presented. It confirms the analytic results on their theoretical
	performance levels },
  doi = {10.1109/29.57542},
  file = {Stoica1990a.pdf:Xeye/music_ML_CRB/Stoica1990a.pdf:PDF},
  issn = {0096-3518},
  keywords = {MLE;MODE-1;MODE-2;MUSIC-1;MUSIC-2;array processing;direction-of-arrival
	estimation;maximum likelihood methods;multiple signal classification;sample
	noise space eigenvectors;eigenvalues and eigenfunctions;parameter
	estimation;signal processing;},
  owner = {postgres},
  timestamp = {2012.05.10}
}

@ELECTRONIC{stokley2007,
  author = {Judy Stokley},
  year = {2007},
  title = {Air Armament 2020: Strategies for the Warfighter},
  language = {English},
  howpublished = {Gulf Coast Chapter of the National Defense Industrial Association},
  organization = {Deputy AFPEO for Weapons and Executive Director Air Armament Center},
  file = {stokley2007.pdf:stokley2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.21}
}

@ARTICLE{Stone2001,
  author = {James V. Stone},
  title = {Blind Source Separation Using Temporal Predictability},
  journal = {Neural Computation},
  year = {2001},
  volume = {13},
  pages = {1559--1574},
  __markedentry = {[postgres:1]},
  file = {Stone2001.pdf:sbl/Stone2001.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.11.18}
}

@ARTICLE{Storlie2009,
  author = {Storlie, Curtis B. and Lee, Thomas C. M. and Hannig, Jan and Nychka,
	Douglas},
  title = {{TRACKING OF MULTIPLE MERGING AND SPLITTING TARGETS: A STATISTICAL
	PERSPECTIVE}},
  journal = {{STATISTICA SINICA}},
  year = {{2009}},
  volume = {{19}},
  pages = {{1-31}},
  number = {{1}},
  month = {{JAN}},
  abstract = {{This article considers the important problem of tracking multiple
	Moving targets captured in image sequences. It has two primary objectives.
	The first is to serve as an introduction Of the target tracking problem
	to the statistical community. It achieves this by providing a common
	definition of the tracking problem, a survey of important existing
	work, and a discussion of the relative advantages and shortcomings
	of such work. The second objective is to propose a statistical method
	for solving a wide class of tracking problems, namely, when the system
	of interest contains birth, death, merging and splitting of targets.
	The stochastic model behind this method is continuous time in nature
	and is equipped with a realistic mechanism for handling merging and
	splitting. Its finite sample properties are assessed via numerical
	experiments. Finally, the method is applied to two scientific problems
	for which it was originally designed: the tracking of (i) storms
	captured in radar reflectivity image data, and (ii) vertexes from
	a high-resolution simulated vorticity field.}},
  file = {Storlie2009.pdf:/home/postgres/doctor/lib/Storlie2009.pdf:PDF},
  issn = {{1017-0405}},
  owner = {postgres},
  timestamp = {2011.06.07},
  unique-id = {{ISI:000262690000001}}
}

@ARTICLE{Tan2012,
  author = {Qun Feng Tan and Shrikanth S. Narayanan},
  title = {Novel Variations of Group Sparse Regularization Techniques With Applications
	to Noise Robust Automatic Speech Recognition},
  journal = {IEEE Transactions on Audio, Speech, and Language Processing},
  year = {2012},
  volume = {20(4)},
  pages = {1337--1346}
}

@TECHREPORT{lex2001,
  author = {Loren Thompson},
  title = {{Aircraft Carrier (In)Vulnerability : What it takes to successfully
	attack an American Aircraft Carrier}},
  institution = {Lexington Institute},
  year = {2001},
  file = {lex2001.pdf:lex2001.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.19}
}

@ELECTRONIC{Tibshirani2011a,
  author = {Robert Tibshirani},
  title = {The Lasso: some novel algorithms and applications},
  __markedentry = {[postgres:]},
  file = {Tibshirani2011a.pdf:blasso/Tibshirani2011a.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Tibshirani2011,
  author = {Robert Tibshirani},
  title = {Regression shrinkage and selection via the lasso},
  journal = {J. R. Statist. Soc. B},
  year = {2011},
  volume = {73},
  pages = {273--282},
  __markedentry = {[postgres:]},
  file = {Tibshirani2011.pdf:blasso/Tibshirani2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@CONFERENCE{Tipping2004,
  author = {Michael E. Tipping},
  title = {Bayesian Inference : An Introduction to Principles and Practice in
	Machine Learning},
  booktitle = {Advanced Lectures on Machine Learning},
  year = {2004},
  editor = {O. Bousquet and U. von Luxburg and G. Ratsch},
  pages = {41-62},
  publisher = {Springer},
  __markedentry = {[postgres:1]},
  file = {Tipping2004.pdf:sbl/Tipping2004.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Tipping2001,
  author = {Michael E. Tipping},
  title = {Sparse Bayesian Learning and the Relevance Vector Machine},
  journal = {Journal of Machine Learning Research},
  year = {2001},
  volume = {1},
  pages = {211--244},
  __markedentry = {[postgres:1]},
  file = {Tipping2001.pdf:sbl/Tipping2001.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@CONFERENCE{Tipping2003,
  author = {Michael E. Tipping and Anita C. Faul},
  title = {Fast Marginal Likelihood Maximisation for Sparse Bayesian Models},
  booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence
	and Statistics},
  year = {2003},
  editor = {C. M. Bishop and B. J. Frey},
  pages = {3-6},
  address = {Key West, FL},
  __markedentry = {[postgres:1]},
  file = {Tipping2003.pdf:sbl/Tipping2003.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Tropp2005,
  author = {Joel A. Tropp and Anna C. Gilbert and Martin J. Strauss},
  title = {Algorithms for Simultaneous Sparse Approximation Part I: Greedy Pursuit},
  journal = {IEEE Transactions on Signal Processing},
  __markedentry = {[postgres:]},
  file = {Tropp2005.pdf:sbl/Tropp2005.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Tropp2005a,
  author = {Joel A. Tropp and Anna C. Gilbert and Martin J. Strauss},
  title = {Algorithms for Simultaneous Sparse Approximation Part II: Convex
	Relaxation},
  journal = {IEEE Transactions on Signal Processing},
  __markedentry = {[postgres:]},
  file = {Tropp2005a.pdf:sbl/Tropp2005a.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Tzikas2008,
  author = {Dimitris G. Tzikas and Aristidis C. Likas and Nikolaos P. Galatsanos},
  title = {The Variational Approximation for Bayesian Inference},
  journal = {IEEE Signal Processing Magazine},
  year = {2008},
  volume = {November},
  pages = {131--146},
  __markedentry = {[postgres:]},
  file = {Tzikas2008.pdf:sbl/Tzikas2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.17}
}

@ARTICLE{van2008probing,
  author = {Van Den Berg, E. and Friedlander, M.P.},
  title = {Probing the Pareto frontier for basis pursuit solutions},
  journal = {SIAM Journal on Scientific Computing},
  year = {2008},
  volume = {31},
  pages = {890--912},
  number = {2}
}

@INPROCEEDINGS{Vaswani2009,
  author = {Vaswani, N.},
  title = {Analyzing Least Squares and Kalman Filtered Compressed Sensing},
  booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE
	International Conference on},
  year = {2009},
  pages = {3013 -3016},
  month = {april},
  abstract = {In recent work, we studied the problem of causally reconstructing
	time sequences of spatially sparse signals, with unknown and slow
	time-varying sparsity patterns, from a limited number of linear ldquoincoherentrdquo
	measurements. We proposed a solution called Kalman filtered compressed
	sensing (KF-CS). The key idea is to run a reduced order KF only for
	the current signal's estimated nonzero coefficients' set, while performing
	CS on the Kalman filtering error to estimate new additions, if any,
	to the set. KF may be replaced by least squares (LS) estimation and
	we call the resulting algorithm LS-CS. In this work, (a) we bound
	the error in performing CS on the LS error and (b) we obtain the
	conditions under which the KF-CS (or LS-CS) estimate converges to
	that of a genie-aided KF (or LS), i.e. the KF (or LS) which knows
	the true nonzero sets.},
  doi = {10.1109/ICASSP.2009.4960258},
  file = {Vaswani2009.pdf:sbl/Vaswani2009.pdf:PDF},
  issn = {1520-6149},
  keywords = {Kalman filtered compressed sensing;Kalman filtering error;causal time
	sequence reconstruction;genie-aided KF;least squares estimation;linear
	incoherent measurements;reduced order KF;signal estimated nonzero
	coefficient set;slow time-varying sparsity pattern;spatially sparse
	signals;Kalman filters;data compression;least squares approximations;reduced
	order systems;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@INPROCEEDINGS{Vaswani2008,
  author = {Vaswani, N.},
  title = {Kalman filtered Compressed Sensing},
  booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference
	on},
  year = {2008},
  pages = {893 -896},
  month = {oct.},
  abstract = {We consider the problem of reconstructing time sequences of spatially
	sparse signals (with unknown and time-varying sparsity patterns)
	from a limited number of linear "incoherent" measurements, in real-time.
	The signals are sparse in some transform domain referred to as the
	sparsity basis. For a single spatial signal, the solution is provided
	by Compressed Sensing (CS). The question that we address is, for
	a sequence of sparse signals, can we do better than CS, if (a) the
	sparsity pattern of the signal's transform coefficients' vector changes
	slowly over time, and (b) a simple prior model on the temporal dynamics
	of its current non-zero elements is available. The overall idea of
	our solution is to use CS to estimate the support set of the initial
	signal's transform vector. At future times, run a reduced order Kalman
	filter with the currently estimated support and estimate new additions
	to the support set by applying CS to the Kalman innovations or filtering
	error (whenever it is "large").},
  doi = {10.1109/ICIP.2008.4711899},
  file = {Vaswani2008.pdf:sbl/Vaswani2008.pdf:PDF},
  issn = {1522-4880},
  keywords = {Kalman filtered compressed sensing;reduced order Kalman filter;signal
	transform vector;sparsity basis;sparsity pattern;spatially sparse
	signals;time sequence reconstruction;transform domain;Kalman filters;least
	mean squares methods;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@PHDTHESIS{Vo2008,
  author = {Ba Tuong Vo},
  title = {{Random Finite Sets in Multi-Object Filtering}},
  school = {{The University of Western Australia}},
  year = {2008},
  address = {Australia},
  file = {Vo2008.pdf:/home/postgres/doctor/lib/Vo2008.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.06.16}
}

@INPROCEEDINGS{Waxman2004,
  author = {Waxman, MJ and Drummond, OE},
  title = {{A bibliography of cluster (group) tracking}},
  booktitle = {{SIGNAL AND DATA PROCESSING OF SMALL TARGETS 2004}},
  year = {{2004}},
  editor = {{Drummond, OE}},
  volume = {{5428}},
  series = {{PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
	(SPIE)}},
  pages = {{551-560}},
  address = {{1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA}},
  organization = {{SPIE}},
  publisher = {{SPIE-INT SOC OPTICAL ENGINEERING}},
  note = {{16th Conference on Signal and Data Processing of Small Targets,
	Orlando, FL, APR 13-15, 2004}},
  abstract = {{Cluster Tracking (also called group tracking) is a key approach to
	greatly reducing the required data communication and processing loads
	that can result from the extreme amount of ambiguous data that might
	be generated by radar and IR sensors in the early post boost phase
	of a ballistic missile. Cluster tracking is especially appropriate
	in tracking a mixture of resolved and unresolved objects as a cluster
	and simplifies the processing for initiating individual tracks when
	many of the target measurements are resolved. This paper presents
	the bibliography that resulted from a literature search on cluster
	tracking using data from one or more sensors. Although the focus
	was on cluster tracking, the literature search also uncovered papers
	on formation tracking and track partitioning (track clustering) and
	those papers are included in the bibliography. The paper also includes
	an introduction that provides an overview of cluster tracking and
	the advantages of tracking clusters. This includes a discussion of
	the four major types of cluster tracking and the uses and distinction
	between cluster tracking, formation tracking, and track clusters
	(partitions).}},
  affiliation = {{Waxman, MJ (Reprint Author), CyberRnD Inc, 10705 Cranks Rd, Culver
	City, CA 90230 USA. CyberRnD Inc, Culver City, CA 90230 USA.}},
  doc-delivery-number = {{BAX35}},
  doi = {{10.1117/12.548357}},
  file = {:/home/postgres/doctor/lib/waxman2004.pdf:PDF},
  isbn = {{0-8194-5351-X}},
  issn = {{0277-786X}},
  keywords = {{target cluster tracking; group tracking; formation tracking; track
	partitioning; tracking closely spaced objects}},
  language = {{English}},
  number-of-cited-references = {{8}},
  owner = {postgres},
  subject-category = {{Optics}},
  times-cited = {{8}},
  timestamp = {2010.12.29},
  type = {{Proceedings Paper}},
  unique-id = {{ISI:000224077200048}}
}

@TECHREPORT{Williams1994,
  author = {Peter M. Williams},
  title = {Bayesian Regularisation and Pruning using a Laplace Prior},
  institution = {School of Cognitive and Computing Sciences, University of Sussex},
  year = {1994},
  type = {Cognitive Science Research Paper, CSRP--312},
  __markedentry = {[postgres:1]},
  file = {Williams1994.pdf:sbl/Williams1994.pdf:PDF},
  owner = {postgres},
  review = {See.
	
	
	对比参见MacKay1990 Beysian 约束的通用形式，然后本文主要工作在于Laplace先验},
  timestamp = {2012.08.18}
}

@CONFERENCE{Wipf2007a,
  author = {David Wipf and Sirkantan Nagarajan},
  title = {A New View of Automatic Relevance Determination},
  booktitle = {The 21 Annu. Conf. Neural Inf. Process. Syst., Vancouver, BC, Canada},
  year = {2007},
  __markedentry = {[postgres:1]},
  file = {Wipf2007a.pdf:sbl/Wipf2007a.pdf:PDF},
  journal = {Neural Inform. Process. Syst.},
  owner = {postgres},
  timestamp = {2012.09.12}
}

@CONFERENCE{Wipf2004a,
  author = {David Wipf and Jason Palmer and Bhaskar D. Rao},
  title = {Perspective on Sparse Bayesian Learning},
  booktitle = {Neural Inf. Process. Syst.},
  year = {2004},
  volume = {16},
  __markedentry = {[postgres:1]},
  file = {Wipf2004a.pdf:sbl/Wipf2004a.pdf:PDF},
  journal = {Neural Inform. Process. Syst.},
  owner = {postgres},
  timestamp = {2012.08.16}
}

@ARTICLE{Wipf2011,
  author = {Wipf, D.P. and Rao, B.D. and Nagarajan, S.},
  title = {Latent Variable Bayesian Models for Promoting Sparsity},
  journal = {Information Theory, IEEE Transactions on},
  year = {2011},
  volume = {57},
  pages = {6236 -6255},
  number = {9},
  month = {sept. },
  __markedentry = {[postgres:1]},
  abstract = {Many practical methods for finding maximally sparse coefficient expansions
	involve solving a regression problem using a particular class of
	concave penalty functions. From a Bayesian perspective, this process
	is equivalent to maximum a posteriori (MAP) estimation using a sparsity-inducing
	prior distribution (Type I estimation). Using variational techniques,
	this distribution can always be conveniently expressed as a maximization
	over scaled Gaussian distributions modulated by a set of latent variables.
	Alternative Bayesian algorithms, which operate in latent variable
	space leveraging this variational representation, lead to sparse
	estimators reflecting posterior information beyond the mode (Type
	II estimation). Currently, it is unclear how the underlying cost
	functions of Type I and Type II relate, nor what relevant theoretical
	properties exist, especially with regard to Type II. Herein a common
	set of auxiliary functions is used to conveniently express both Type
	I and Type II cost functions in either coefficient or latent variable
	space facilitating direct comparisons. In coefficient space, the
	analysis reveals that Type II is exactly equivalent to performing
	standard MAP estimation using a particular class of dictionary- and
	noise-dependent, nonfactorial coefficient priors. One prior (at least)
	from this class maintains several desirable advantages over all possible
	Type I methods and utilizes a novel, nonconvex approximation to the
	/l/_0 norm with most, and in certain quantifiable conditions all,
	local minima smoothed away. Importantly, the global minimum is always
	left unaltered unlike standard /l/_1 -norm relaxations. This ensures
	that any appropriate descent method is guaranteed to locate the maximally
	sparse solution.},
  doi = {10.1109/TIT.2011.2162174},
  file = {Wipf2011.pdf:sbl/Wipf2011.pdf:PDF},
  issn = {0018-9448},
  keywords = {MAP estimation;alternative Bayesian algorithm;auxiliary functions;coefficient
	space;concave penalty function;latent variable Bayesian model;maximization;maximum
	a posteriori estimation;nonconvex approximation;regression problem;scaled
	Gaussian distribution;sparsity-inducing prior distribution;variational
	representation;variational techniques;Gaussian distribution;Regge
	poles;concave programming;estimation theory;information theory;regression
	analysis;sparse matrices;variational techniques;},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@PHDTHESIS{Wipf2006,
  author = {David Paul Wipf},
  title = {Bayesian Methods for Finding Sparse Representations},
  school = {University of California, San Diego},
  year = {2006},
  __markedentry = {[postgres:1]},
  file = {Wipf2006.pdf:sbl/Wipf2006.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.15}
}

@ARTICLE{Wipf2010a,
  author = {David P. Wipf and Srikantan S. Nagarajan},
  title = {Iterative Reweighted $\ell_1$ and $\ell_2$ Methods for Finding Sparse
	Solutions},
  journal = {IEEE Transactions on Signal Processing},
  __markedentry = {[postgres:]},
  file = {Wipf2010a.pdf:blasso/Wipf2010a.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.17}
}

@ARTICLE{Wipf2010,
  author = {David P. Wipf and Julia P. Owen and Hagai T. Attias and Kensuke Sekihara
	and Srikantan S. Nagarajan},
  title = {Robust Bayesian estimation of the location, orientation, and time
	course of multiple correlated neural sources using MEG},
  journal = {NeuroImage},
  year = {2010},
  volume = {49},
  pages = {641--655},
  number = {1},
  __markedentry = {[postgres:1]},
  abstract = {The synchronous brain activity measured via MEG (or EEG) can be interpreted
	as arising from a collection (possibly large) of current dipoles
	or sources located throughout the cortex. Estimating the number,
	location, and time course of these sources remains a challenging
	task, one that is significantly compounded by the effects of source
	correlations and unknown orientations and by the presence of interference
	from spontaneous brain activity, sensor noise, and other artifacts.
	This paper derives an empirical Bayesian method for addressing each
	of these issues in a principled fashion. The resulting algorithm
	guarantees descent of a cost function uniquely designed to handle
	unknown orientations and arbitrary correlations. Robust interference
	suppression is also easily incorporated. In a restricted setting,
	the proposed method is shown to produce theoretically zero reconstruction
	error estimating multiple dipoles even in the presence of strong
	correlations and unknown orientations, unlike a variety of existing
	Bayesian localization methods or common signal processing techniques
	such as beamforming and sLORETA. Empirical results on both simulated
	and real data sets verify the efficacy of this approach.},
  doi = {10.1016/j.neuroimage.2009.06.083},
  file = {Wipf2010.pdf:sbl/Wipf2010.pdf:PDF},
  issn = {1053-8119},
  owner = {postgres},
  timestamp = {2012.08.18},
  url = {http://www.sciencedirect.com/science/article/pii/S105381190900696X}
}

@ARTICLE{Wipf2007,
  author = {David P. Wipf and Rao, B.D.},
  title = {An Empirical Bayesian Strategy for Solving the Simultaneous Sparse
	Approximation Problem},
  journal = {Signal Processing, IEEE Transactions on},
  year = {2007},
  volume = {55},
  pages = {3704 -3716},
  number = {7},
  month = {july },
  __markedentry = {[postgres:1]},
  doi = {10.1109/TSP.2007.894265},
  file = {Wipf2007.pdf:sbl/Wipf2007.pdf:PDF},
  issn = {1053-587X},
  keywords = {automatic relevance determination;coefficient expansions;cost function;empirical
	Bayesian strategy;global minima;local minima;posterior distribution;signal
	vectors;simultaneous sparse approximation problem;source localization;sparse
	Bayesian learning;sparse representation problem;Bayes methods;learning
	(artificial intelligence);maximum likelihood estimation;signal representation;},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Wipf2004,
  author = {David P. Wipf and Rao, B.D.},
  title = {Sparse Bayesian learning for basis selection},
  journal = {Signal Processing, IEEE Transactions on},
  year = {2004},
  volume = {52},
  pages = { 2153 - 2164},
  number = {8},
  month = {aug.},
  __markedentry = {[postgres:1]},
  abstract = {Sparse Bayesian learning (SBL) and specifically relevance vector machines
	have received much attention in the machine learning literature as
	a means of achieving parsimonious representations in the context
	of regression and classification. The methodology relies on a parameterized
	prior that encourages models with few nonzero weights. In this paper,
	we adapt SBL to the signal processing problem of basis selection
	from overcomplete dictionaries, proving several results about the
	SBL cost function that elucidate its general behavior and provide
	solid theoretical justification for this application. Specifically,
	we have shown that SBL retains a desirable property of the #8467;_0
	-norm diversity measure (i.e., the global minimum is achieved at
	the maximally sparse solution) while often possessing a more limited
	constellation of local minima. We have also demonstrated that the
	local minima that do exist are achieved at sparse solutions. Later,
	we provide a novel interpretation of SBL that gives us valuable insight
	into why it is successful in producing sparse representations. Finally,
	we include simulation studies comparing sparse Bayesian learning
	with basis pursuit and the more recent FOCal Underdetermined System
	Solver (FOCUSS) class of basis selection algorithms. These results
	indicate that our theoretical insights translate directly into improved
	performance.},
  doi = {10.1109/TSP.2004.831016},
  file = {Wipf2004.pdf:sbl/Wipf2004.pdf:PDF},
  issn = {1053-587X},
  keywords = { basis pursuit; basis selection algorithms; diversity measures; focal
	undetermined system solver; linear inverse problems; machine learning;
	overcomplete dictionaries; relevance vector machines; signal processing;
	sparse Bayesian learning; Bayes methods; inverse problems; iterative
	methods; learning (artificial intelligence); signal representation;},
  owner = {postgres},
  timestamp = {2012.07.06}
}

@ARTICLE{Wright2009,
  author = {Wright, J. and Yang, A.Y. and Ganesh, A. and Sastry, S.S. and Yi
	Ma},
  title = {Robust Face Recognition via Sparse Representation},
  journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  year = {2009},
  volume = {31},
  pages = {210 -227},
  number = {2},
  month = {feb. },
  abstract = {We consider the problem of automatically recognizing human faces from
	frontal views with varying expression and illumination, as well as
	occlusion and disguise. We cast the recognition problem as one of
	classifying among multiple linear regression models and argue that
	new theory from sparse signal representation offers the key to addressing
	this problem. Based on a sparse representation computed by C^1 -minimization,
	we propose a general classification algorithm for (image-based) object
	recognition. This new framework provides new insights into two crucial
	issues in face recognition: feature extraction and robustness to
	occlusion. For feature extraction, we show that if sparsity in the
	recognition problem is properly harnessed, the choice of features
	is no longer critical. What is critical, however, is whether the
	number of features is sufficiently large and whether the sparse representation
	is correctly computed. Unconventional features such as downsampled
	images and random projections perform just as well as conventional
	features such as eigenfaces and Laplacianfaces, as long as the dimension
	of the feature space surpasses certain threshold, predicted by the
	theory of sparse representation. This framework can handle errors
	due to occlusion and corruption uniformly by exploiting the fact
	that these errors are often sparse with respect to the standard (pixel)
	basis. The theory of sparse representation helps predict how much
	occlusion the recognition algorithm can handle and how to choose
	the training images to maximize robustness to occlusion. We conduct
	extensive experiments on publicly available databases to verify the
	efficacy of the proposed algorithm and corroborate the above claims.},
  doi = {10.1109/TPAMI.2008.79},
  file = {Wright2009.pdf:sbl/Wright2009.pdf:PDF},
  issn = {0162-8828},
  keywords = {Laplacianfaces;downsampled images;eigenfaces;feature extraction;illumination;image-based
	object recognition;multiple linear regression model;occlusion;random
	projections;robust face recognition;sparse signal representation;face
	recognition;feature extraction;lightning;object recognition;random
	processes;regression analysis;signal representation;Algorithms;Artificial
	Intelligence;Biometry;Cluster Analysis;Face;Humans;Image Enhancement;Image
	Interpretation, Computer-Assisted;Pattern Recognition, Automated;Reproducibility
	of Results;Sensitivity and Specificity;Subtraction Technique;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Yang2012,
  author = {Zai Yang and Lihua Xie and Cishen Zhang},
  title = {Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference},
  journal = {Arxiv:1108.5838v3},
  year = {2012},
  volume = {Mar},
  pages = {1--13},
  __markedentry = {[postgres:]},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@ARTICLE{Yang2011,
  author = {Zai Yang and Lihua Xie and Cishen Zhang},
  title = {Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference},
  journal = {Arxiv:1108.5838v2},
  year = {2011},
  volume = {Sep},
  pages = {1--23},
  __markedentry = {[postgres:]},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@ARTICLE{Yuan2006,
  author = {M. Yuan and Y. Lin},
  title = {Model selection and estimation in regression with grouped variables},
  journal = {J. R. Statist. Soc. B},
  year = {2006},
  volume = {68},
  pages = {49--67},
  __markedentry = {[postgres:1]},
  file = {Yuan2006.pdf:sbl/Yuan2006.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.10.24}
}

@ARTICLE{Zhang_TBME2012a,
  author = {Zhilin Zhang and Tzyy-Ping Jung and Scott Makeig and Bhaskar D. Rao},
  title = {Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy
	Consumption and Inexpensive Hardware},
  year = {2012},
  pages = {1--4},
  __markedentry = {[postgres:]},
  file = {Zhang_TBME2012a.pdf:sbl/Zhang_TBME2012a.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.17}
}

@ARTICLE{Zhang_TBME2012b,
  author = {Zhilin Zhang and Tzyy-Ping Jung and Scott Makeig and Bhaskar D. Rao},
  title = {Compressed Sensing for Energy-Efficient Wireless Telemonitoring of
	Non-Invasive Fetal {ECG} via Block Sparse {Bayesian} Learning},
  journal = {to appear in IEEE Transaction on Biomedical Engineering},
  year = {2012},
  __markedentry = {[postgres:]},
  file = {Zhang_TBME2012b.pdf:sbl/Zhang_TBME2012b.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.17}
}

@INPROCEEDINGS{zhang2010sparse,
  author = {Zhang, Z. and Rao, B.D.},
  title = {Sparse signal recovery in the presence of correlated multiple measurement
	vectors},
  booktitle = {Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International
	Conference on},
  year = {2010},
  pages = {3986--3989},
  organization = {IEEE}
}

@TECHREPORT{Zhang2012,
  author = {Zhilin Zhang and Bhaskar D. Rao},
  title = {Clarify Some Issues on the Sparse Bayesian Learning for Sparse Signal
	Recovery},
  institution = {University of California at San Diego},
  year = {2012},
  __markedentry = {[postgres:1]},
  file = {Zhang2012.pdf:sbl/Zhang2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.10}
}

@ARTICLE{Zhang2012a,
  author = {Zhilin Zhang and Bhaskar D. Rao},
  title = {Extension of {SBL} Algorithms for the Recovery of Block Sparse Signals
	with Intra-Block Correlation},
  journal = {to appear in IEEE Transactions on Signal Processing},
  year = {2012},
  __markedentry = {[postgres:1]},
  file = {Zhang2012a.pdf:sbl/Zhang2012a.pdf:PDF},
  owner = {postgres},
  review = {bSBL-EM --> done
	
	bSBL-BO --> done
	
	bSBL-L1},
  timestamp = {2012.08.13}
}

@ARTICLE{Zhang2011,
  author = {Zhilin Zhang and Bhaskar D. Rao},
  title = {Sparse Signal Recovery with Temporally Correlated Source Vectors
	Using Sparse Bayesian Learning},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  year = {2011},
  volume = {5},
  pages = {912--926},
  number = {5},
  __markedentry = {[postgres:1]},
  file = {Zhang2011.pdf:sbl/Zhang2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.08.10}
}

@ARTICLE{Zheng2003,
  author = {Yibin Zheng and Shu-Ming Tseng and Kai-Bor Yu},
  title = {Closed-form four-channel monopulse two-target resolution},
  journal = {Aerospace and Electronic Systems, IEEE Transactions on},
  year = {2003},
  volume = {39},
  pages = { 1083 - 1089},
  number = {3},
  month = {july},
  abstract = { A novel closed-form solution to resolve the directions of arrival
	(azimuth and elevation) of two sources using a single snapshot (monopulse)
	of four independent channels is presented. Both phase comparison
	monopulse and amplitude comparison monopulse are solved. Exceptions
	where the two targets cannot be resolved are also discussed. Numerical
	simulation result of a practical phased-array configuration validates
	the effectiveness of the new solution.},
  doi = {10.1109/TAES.2003.1238760},
  file = {:zheng2003.pdf:PDF},
  issn = {0018-9251},
  keywords = { amplitude comparison monopulse; closed-form solution; directions
	of arrival; four-channel monopulse two-target resolution; independent
	channels; monopulse; phase comparison monopulse; phased-array configuration;
	antenna phased arrays; direction-of-arrival estimation; radar detection;
	target tracking;},
  owner = {postgres},
  timestamp = {2011.08.07}
}

@ARTICLE{Zhou2011,
  author = {Jiayu Zhou and Lei Yuan and Jun Liu and Jieping Ye},
  title = {A Multi-Task Learning Formulation for Predicting Disease Progression},
  journal = {KDD},
  year = {2011},
  volume = {1},
  pages = {1--9},
  __markedentry = {[postgres:1]},
  file = {Zhou2011.pdf:sbl/Zhou2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.11.18}
}

@INPROCEEDINGS{Ziniel2010,
  author = {Ziniel, J. and Potter, L.C. and Schniter, P.},
  title = {Tracking and smoothing of time-varying sparse signals via approximate
	belief propagation},
  booktitle = {Signals, Systems and Computers (ASILOMAR), 2010 Conference Record
	of the Forty Fourth Asilomar Conference on},
  year = {2010},
  pages = {808 -812},
  month = {nov.},
  abstract = {This paper considers the problem of recovering time-varying sparse
	signals from dramatically undersampled measurements. A probabilistic
	signal model is presented that describes two common traits of time-varying
	sparse signals: a support set that changes slowly over time, and
	amplitudes that evolve smoothly in time. An algorithm for recovering
	signals that exhibit these traits is then described. Built on the
	belief propagation framework, the algorithm leverages recently developed
	approximate message passing techniques to perform rapid and accurate
	estimation. The algorithm is capable of performing both causal tracking
	and non-causal smoothing to enable both online and offline processing
	of sparse time series, with a complexity that is linear in all problem
	dimensions. Simulation results illustrate the performance gains obtained
	through exploiting the temporal correlation of the time series relative
	to independent recoveries.},
  doi = {10.1109/ACSSC.2010.5757677},
  file = {Ziniel2010.pdf:sbl/Ziniel2010.pdf:PDF},
  issn = {1058-6393},
  keywords = {belief propagation framework;message passing technique;probabilistic
	signal model;sparse time series;temporal correlation;time series;time-varying
	sparse signal;probability;signal processing;time series;},
  owner = {postgres},
  timestamp = {2012.08.18}
}

@ARTICLE{Zou2006,
  author = {Hui Zou},
  title = {The Adaptive Lasso and its Oracle Properties},
  journal = {Journal of American Statistical Association},
  year = {2006},
  volume = {101(476)},
  pages = {1418--1429},
  __markedentry = {[postgres:]},
  file = {Zou2006.pdf:blasso/Zou2006.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{Zou2007,
  author = {Hui Zou and Trevor Hastie and Robert Tibshirani},
  title = {on the ``Degrees of Freedom'' of the lasso},
  journal = {The annals of statistics},
  year = {2007},
  volume = {35(5)},
  pages = {2173--2192},
  __markedentry = {[postgres:]},
  file = {Zou2007.pdf:blasso/Zou2007.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.09.24}
}

@ARTICLE{he2009,
  author = {何传易 and 卢再奇},
  title = {拖曳式诱饵干扰关键参数分析},
  journal = {航天电子对抗},
  year = {2009},
  volume = {25 (4)},
  pages = {{11-13}},
  file = {he2009.pdf:trad/he2009.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@ARTICLE{hou2010,
  author = {候向辉 and 刘晓东 and 李仙茂},
  title = {拖曳式诱饵诱骗防空导弹探讨},
  journal = {舰船电子对抗},
  year = {2010},
  volume = {33 (1)},
  pages = {{40-43}},
  file = {hou2010.pdf:trad/hou2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@ARTICLE{hou2010a,
  author = {候向辉 and 刘晓东 and 饶志高 and 李仙茂},
  title = {拖曳式诱饵释放时机和释放过程研究},
  journal = {航天电子对抗},
  year = {2010},
  volume = {26 (2)},
  pages = {{6-8}},
  file = {hou2010a.pdf:trad/hou2010a.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@PHDTHESIS{Liu2012,
  author = {刘章孟},
  title = {基于信号空域稀疏性的阵列处理理论与方法},
  school = {国防科学技术大学},
  year = {2012},
  __markedentry = {[postgres:]},
  file = {Liu2012.pdf:sbl/Liu2012.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.09}
}

@PHDTHESIS{meng2010,
  author = {孟凡彬},
  title = {基于随机集理论的多目标跟踪技术研究},
  school = {哈尔滨工程大学},
  year = {2010},
  address = {黑龙江,哈尔滨},
  file = {meng2010.pdf:/home/postgres/doctor/lib/meng2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.06.15}
}

@ARTICLE{song2011a,
  author = {宋志勇 and 肖怀铁},
  title = {基于回波幅度特征的拖曳式诱饵存在性检测},
  journal = {电子与信息学报},
  year = {2011},
  volume = {33 (6)},
  pages = {{1515-1519}},
  file = {song2011a.pdf:trad/song2011a.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@ARTICLE{song2011,
  author = {宋志勇 and 肖怀铁 and 祝依龙 and 卢再奇},
  title = {基于扩展单脉冲比的拖曳式诱饵存在性检测},
  journal = {航空学报},
  year = {2011},
  volume = {32 (9)},
  pages = {{1656-1668}},
  file = {song2011.pdf:trad/song2011.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@PHDTHESIS{zhang2009,
  author = {张昌芳},
  title = {阵群目标信息相关技术研究},
  school = {国防科学技术大学},
  year = {2009},
  address = {湖南, 长沙},
  file = {zhang2009.pdf:/home/postgres/doctor/lib/zhang2009.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.01.07}
}

@ARTICLE{yang2010,
  author = {杨利民 and 苏卫民 and 顾红},
  title = {基于脉组间频率步进的合成超宽带距离像及速度分析},
  journal = {航空学报},
  year = {2010},
  volume = {31(10)},
  pages = {{2046-2055}},
  owner = {postgres},
  timestamp = {2011.06.13}
}

@ELECTRONIC{Yang2006,
  author = {杨善泳},
  month = {June},
  year = {2006},
  title = {A Tutorial on Relevance Vector Machine},
  language = {Chinese},
  file = {Yang2006.pdf:sbl/Yang2006.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.06.08}
}

@BOOK{Wang2004,
  title = {空间谱估计理论与算法},
  publisher = {清华大学出版社},
  year = {2004},
  author = {王永良 and 陈辉 and 彭应宁 and 万群},
  __markedentry = {[postgres:]},
  owner = {postgres},
  timestamp = {2012.06.06}
}

@ARTICLE{wang2001,
  author = {王祖林 and 张孟 and 段世忠 and 周荫清},
  title = {比相单脉冲雷达测角与角闪烁研究},
  journal = {航空学报},
  year = {2001},
  volume = {22 sup.},
  pages = {26--29},
  file = {wang2001.pdf:wang2001.pdf:PDF},
  owner = {postgres},
  timestamp = {2012.03.10}
}

@MASTERSTHESIS{wang2009,
  author = {王芝},
  title = {基于概率假设密度函数({PHD})的多目标跟踪算法研究},
  school = {杭州电子科技大学},
  year = {2009},
  address = {浙江, 杭州},
  file = {wang2009.pdf:/home/postgres/doctor/lib/wang2009.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.06.18}
}

@ARTICLE{bai2009,
  author = {白谓雄 and 焦光龙 and 付红卫},
  title = {拖曳式诱饵对抗技术研究},
  journal = {系统工程与电子技术},
  year = {2009},
  volume = {31 (3)},
  pages = {{579-582}},
  file = {bai2009.pdf:trad/bai2009.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@PHDTHESIS{luo2010,
  author = {罗飞腾},
  title = {目标跟踪的粒子滤波技术研究},
  school = {中国科学技术大学},
  year = {2010},
  address = {安徽, 合肥},
  file = {luo2010.pdf:/home/postgres/doctor/lib/luo2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.06.15}
}

@ARTICLE{lu2010,
  author = {芦艳龙 and 童中翔 and 于锦禄 and 蒋赟},
  title = {拖曳式诱饵运动特性建模与仿真机算},
  journal = {飞行力学},
  year = {2010},
  volume = {28 (5)},
  pages = {{24-26}},
  file = {lu2010.pdf:trad/lu2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@ARTICLE{Lian2010a,
  author = {连峰 and 韩崇昭 and 刘伟峰 and 元向辉},
  title = {{基于SMC-PHDP的部分可分辨的群目标跟踪算法}},
  journal = {自动化学报},
  year = {2010},
  volume = {36(5)},
  pages = {{731-741}},
  file = {Lian2010a.pdf:/home/postgres/doctor/lib/Lian2010a.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.06.08}
}

@ARTICLE{guo2010,
  author = {郭颖睿 and 任宏滨 and 李静},
  title = {一种新型拖曳式诱饵技术研究},
  journal = {弹箭与制导学报},
  year = {2010},
  volume = {30 (4)},
  pages = {{76-78}},
  file = {guo2010.pdf:trad/guo2010.pdf:PDF},
  owner = {postgres},
  review = {对于相干两点源干扰而言，对干扰效果产生直接影响的是幅度比与相位差。
	
	通过拖引产生的相位差对导引头的制导回路产生影响。
	
	停拖，拖引期，间断期},
  timestamp = {2011.10.26}
}

@ARTICLE{han2006,
  author = {韩朝 and 赵国志},
  title = {动力型微型角反射器},
  journal = {南京理工大学学报},
  year = {2006},
  volume = {30 (3)},
  pages = {{270-273}},
  file = {han2006.pdf:trad/han2006.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@ARTICLE{gao2010,
  author = {高彬 and 毛士艺 and 孙进平},
  title = {拖曳式诱饵抗单脉冲雷达导引头效能评估},
  journal = {系统工程与电子技术},
  year = {2010},
  volume = {32 (11)},
  pages = {{2394-2397}},
  file = {gao2010.pdf:trad/gao2010.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.10.26}
}

@BOOK{huang2005,
  title = {雷达目标特性},
  publisher = {电子工业出版社},
  year = {2005},
  editor = {黄培康 and 殷红成 and 许小剑},
  author = {黄培康 and 殷红成 and 许小剑},
  owner = {postgres},
  timestamp = {2012.03.09}
}

@MISC{aesa0,
  title = {{AESA Radar:} Revolutionary Capabilities for Multiple Missions},
  file = {aesa0.pdf:aesa0.pdf:PDF},
  owner = {postgres},
  timestamp = {2011.09.15}
}

@ELECTRONIC{msp418k2007,
  year = {2007},
  title = {MSP--418K DRFM Module},
  language = {Russian},
  url = {http://www.cnirti.ru/catalog-10-18.htm},
  owner = {postgres},
  timestamp = {2012.04.11}
}

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